Abstract:
In one embodiment, a method includes maintaining, by a computer system, a plurality of data and at least one classification for each of the plurality of data. Each data of the plurality of data is aggregated from a plurality data sources and is abstracted into one or more of a plurality of standardized formats based on a type of the data. The computer system has executing thereon a plurality of classification engines. The plurality of classification engines include an a priori classification engine, an a posteriori classification engine, and a heuristics engine. Each at least one classification is generated by one of the plurality of classification engines. In addition, the method includes receiving query input from a user. Further, the method includes querying at least a portion of the plurality of data responsive to the query input.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This patent application claims priority from, and incorporates by reference the entire disclosure of, U.S. Provisional Patent Application No. 61/658,034 filed on Jun. 11, 2012. This patent application incorporates by reference the entire disclosure of a U.S. patent application Ser. No. 13/906,241 and filed on May 30, 2013. This patent application also incorporates by reference the entire disclosure of a U.S. patent application Ser. No. 13/906,246 and filed on May 30, 2013. 
    
    
     BACKGROUND 
     1. Technical Field 
     The present invention relates generally to data aggregation and analysis and more particularly, but not by way of limitation, to systems and methods for multi-source data aggregation and analysis. 
     2. History of Related Art 
     Businesses have access to an inordinate amount of data. For example, businesses collect data relating to customers, vendors, sales, inventory, employees, research, competitors, etc. Some of this data is knowingly collected or stored for business purposes. For example, a business may record or store sales numbers for tax reasons and/or for determining whether to expand production or cease production of specific products. However, often there is a large amount of additional data, or metadata, that exists and is accessible by a business, but which is ignored because, for example, the data is difficult to access or the business is unaware that the metadata exists or can be obtained from the data to which the business currently has access. Generally, although not necessarily, this metadata includes mined data that can be extracted from the data that the business collects or generates. Further, this metadata can include trends related to the data available to the business. 
     For instance, a business may have enough data to determine the amount of time spent per dollar earned from each customer. However, the business may not be able to easily extract this information from the data available because, for example, to determine the time spent per dollar earned may require accessing a number of separate systems and performing some amount of data mining or additional processing. In some cases, to determine the time spent per dollar earned from each customer, a business may need to examine sales numbers as well as the time and resources spent by each employee of the business who was involved in generating the sale. The time and resources to generate the sale could include more than just the time spent by a salesperson communicating with the customer. For example, the time and resources could also include the amount of time support staff (e.g., assistants) dedicate for each customer, the amount of time spent by technical support helping the customer, the resources used (e.g., paper, sample products, etc.), the amount of time sales teams spend to determine the best strategy for approaching the customer, delivery costs, etc. 
     Moreover, as the value and use of information continues to increase, individuals and businesses seek additional ways to process and store information. One option available to users is information handling systems. An information handling system generally processes, compiles, stores, and/or communicates information or data for business, personal, or other purposes thereby allowing users to take advantage of the value of the information. Because technology and information handling needs and requirements vary between different users or applications, information handling systems may also vary regarding what information is handled, how the information is handled, how much information is processed, stored, or communicated, and how quickly and efficiently the information may be processed, stored, or communicated. The variations in information handling systems allow for information handling systems to be general or configured for a specific user or specific use such as financial transaction processing, airline reservations, enterprise data storage, or global communications. In addition, information handling systems may include a variety of hardware and software components that may be configured to process, store, and communicate information and may include one or more computer systems, data storage systems, and networking systems. 
     SUMMARY OF THE INVENTION 
     In one embodiment, a method includes maintaining, by a computer system comprising at least one server computer, a plurality of data and at least one classification for each of the plurality of data. Each data of the plurality of data is aggregated from a plurality data sources and is abstracted into one or more of a plurality of standardized formats based on a type of the data. The computer system has executing thereon a plurality of classification engines. The plurality of classification engines include an a priori classification engine, an a posteriori classification engine, and a heuristics engine. Each at least one classification is generated by one of the plurality of classification engines. In addition, the method includes receiving, by the computer system, query input from a user in accordance with at least one standardized format of the plurality of standardized formats. Further, the method includes querying, by the computer system, at least a portion of the plurality of data responsive to the query input. 
     In one embodiment, an information handling system includes a processing unit that is operable to implement a method. The method includes maintaining a plurality of data and at least one classification for each of the plurality of data. Each data of the plurality of data is aggregated from a plurality data sources and is abstracted into one or more of a plurality of standardized formats based on a type of the data. The information handling system has executing thereon a plurality of classification engines. The plurality of classification engines include an a priori classification engine, an a posteriori classification engine, and a heuristics engine. Each at least one classification is generated by one of the plurality of classification engines. In addition, the method includes receiving query input from a user in accordance with at least one standardized format of the plurality of standardized formats. Further, the method includes querying at least a portion of the plurality of data responsive to the query input. 
     In one embodiment, a computer-program product includes a computer-usable medium having computer-readable program code embodied therein. The computer-readable program code is adapted to be executed to implement a method. The method includes maintaining, by a computer system comprising at least one server computer, a plurality of data and at least one classification for each of the plurality of data. Each data of the plurality of data is aggregated from a plurality data sources and is abstracted into one or more of a plurality of standardized formats based on a type of the data. The computer system has executing thereon a plurality of classification engines. The plurality of classification engines include an a priori classification engine, an a posteriori classification engine, and a heuristics engine. Each at least one classification is generated by one of the plurality of classification engines. In addition, the method includes receiving, by the computer system, query input from a user in accordance with at least one standardized format of the plurality of standardized formats. Further, the method includes querying, by the computer system, at least a portion of the plurality of data responsive to the query input. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       A more complete understanding of the method and apparatus of the present invention may be obtained by reference to the following Detailed Description when taken in conjunction with the accompanying Drawings wherein: 
         FIG. 1  illustrates an embodiment of a networked computing environment. 
         FIG. 2  illustrates an embodiment of a Business Insight on Messaging (BIM) system. 
         FIG. 3  presents a flowchart of an example of a data collection process. 
         FIG. 4  presents a flowchart of an example of a data classification process. 
         FIG. 5  presents a flowchart of an example of a data query process. 
         FIG. 6  illustrates an example of a heuristics engine. 
         FIG. 7  presents a flowchart of an example of a heuristics process. 
         FIG. 8  presents a flowchart of an example of a data query process. 
         FIG. 9  illustrates an example of a user interface. 
         FIG. 10  illustrates an example of a user interface. 
     
    
    
     DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS OF THE INVENTION 
     This disclosure describes several non-limiting examples of processes for collecting information or data from multiple sources and analyzing the information to classify the data and to extract or determine additional information based on the collected data. The data sources can be internal to the business and/or external to the business. For example, the data sources can include sales databases, business or internal email systems, non-business or external email systems, social networking accounts, inventory databases, file directories, enterprise systems, customer relationship management (CRM) systems, organizational directories, collaboration systems (e.g., SharePoint™ servers), etc. 
     As used herein, the term “business,” in addition to having its ordinary meaning, is intended to include any type of organization or entity. For example, a business can include a charitable organization, a governmental organization, an educational institution, or any other entity that may have one or more sources of data to analyze. Further, the user of any of the above terms may be used interchangeably unless explicitly used otherwise or unless the context makes clear otherwise. In addition, as used herein, the term “data” generally refers to electronic data or any type of data that can be accessed by a computing system. 
     Additional examples of systems and associated processes that may be used with the present disclosure are included in Appendix A, which is hereby incorporated by reference in its entirety. Further, Appendix A describes additional applications and uses that, in some cases, may be performed by the systems and/or as part of the processes that are described herein. 
     Example of a Networked Computing Environment 
       FIG. 1  illustrates an embodiment of a networked computing environment  100 . The networked computing environment  100  can include a computing environment  102  that is associated with a business or organization. The computing environment  102  may vary based on the type of organization or business. However, generally, the computing environment  102  may include at least a number of computing systems. For example, the computing environment may include clients, servers, databases, mobile computing devices (e.g., tablets, laptops, smartphones, etc.), virtual computing devices, shared computing devices, networked computing devices, and the like. Further, the computing environment  102  may include one or more networks, such as intranet  104 . 
     The computing environment  102  includes a Business Insights on Messaging (BIM) system  130 . Using the BIM system  130 , a user can examine the data available to a business regardless of where the data was generated or is stored. Further, in some embodiments, the user can use the BIM system  130  to identify trends and/or metadata associated with the data available to the BIM system  130 . In certain embodiments, the BIM system  130  can access the data from internal data sources  120 , external data sources  122 , or a combination of the two. The data that can be accessed from the internal data sources  120  can include any data that is stored within the computing environment  102  or is accessed by a computing system that is associated with the computing environment  102 . For example, the data may include information stored in employee created files, log files, archived files, internal emails, outgoing emails, received emails, received files, data downloaded from an external network or the Internet, etc. The type of data is not limited and may depend on the organization or business associated with the computing environment  102 . For example, the data can include sales numbers, contact information, vendor costs, product designs, meeting minutes, the identity of file creators, the identity of file owners, the identity of users who have accessed a file or are authorized to access a file, etc. 
     The data that can be accessed from the external data sources  122  can include any data that is stored outside of the computing environment  102  and is publicly accessible or otherwise accessible to the BIM system  130 . For example, the data can include data from social networking sites, customer sites, Internet sites, or any other data source that is publicly accessible or which the BIM system  130  has been granted access. In some cases, a subset of the data may be unavailable to the BIM system  130 . For example, portions of the computing environment  102  may be configured for private use. 
     The internal data sources  120  can include any type of computing system that is part of or associated with the computing environment  102  and is available to the BIM system  130 . These computing systems can include database systems or repositories, servers (e.g., authentication servers, file servers, email servers, collaboration servers), clients, mobile computing systems (including e.g., tablets, laptops, smartphones, etc.), virtual machines, CRM systems, directory services, such as lightweight directory access protocol (LDAP) systems, and the like. Further, in some cases, the internal data sources  120  can include the clients  114  and  116 . The external data sources  122  can include any type of computing system that is not associated with the computing environment  102 , but is accessible to the BIM system  130 . For example, the external data sources  122  can include any computing systems associated with cloud services, social media services, hosted applications, etc. 
     The BIM system  130  can communicate with the internal data sources  120  via the intranet  104 . The intranet  104  can include any type of wired and/or wireless network that enables computing systems associated with the computing environment  102  to communicate with each other. For example, the intranet  104  can include any type of a LAN, a WAN, an Ethernet network, a wireless network, a cellular network, a virtual private network (VPN) and an ad hoc network. In some embodiments, the intranet  104  may include an extranet that is accessible by customers or other users who are external to the business or organization associated with the computing environment  102 . 
     The BIM system  130  can communicate with the external data sources  122  via the network  106 . The network  106  can include any type of wired, wireless, or cellular network that enables one or more computing systems associated with the computing environment  102  to communicate with the external data sources  122  and/or any computing system that is not associated with the computing environment  102 . In some cases, the network  106  can include the Internet. 
     A user can access the BIM system  130  using any computing system that can communicate with the BIM system  130 . For example, the user can access the BIM system  130  using the client  114 , which can communicate with the BIM system  130  via the intranet  104 , the client  116 , which can communicate via a direct communication connection with the BIM system  130 , or the client  118 , which can communicate with the BIM system  130  via the network  106 . As illustrated in  FIG. 1 , in some embodiments the client  118  may not be associated with the computing environment  102 . In such embodiments, the client  118  and/or a user associated with the client  118  may be granted access to the BIM system  130 . The clients  114 ,  116 , and  118  may include any type of computing system including, for example, a laptop, desktop, smartphone, tablet, or the like. In some embodiments, the BIM system  130  may determine whether the user is authorized to access the BIM system  130  as described in further detail below. 
     The BIM system  130  can include a data collection system  132 , a data classification system  134 , and a BIM access system  136 . The data collection system  132  can collect data or information from one or more data sources for processing by the BIM system  130 . In some embodiments, the data collection system  132  can reformat the collected data to facilitate processing by the BIM system  130 . Further, in some cases, the data collection system  132  may reformat collected data into a consistent or defined format that enables the comparison or processing of data that is of the same or a similar type, but which may be formatted differently because, for example, the data is obtained from different sources. The data collection system  132  is described in more detail below with reference to  FIG. 2 . 
     The data classification system  134  can store and classify the data obtained by the data collection system  132 . In addition to predefined classifications, the data classification system  134  can identify and develop new classifications and associations between data using, for example, heuristics and probabilistic algorithms. The data classification system  134  is described in more detail below with reference to  FIG. 3 . 
     The BIM access system  136  can provide users with access to the BIM system  130 . In some embodiments, the BIM access system  136  determines whether a user is authorized to access the BIM system  130 . The BIM access system  136  enables a user to query one or more databases (not shown) of the data classification system  134  to obtain access to the data collected by the data collection system  132 . Further, the BIM access system  136  enables a user to mine the data and/or to extract metadata by, for example, creating queries based on the data and the data classifications. Advantageously, in certain embodiments, because the data classification system  134  can classify data obtained from a number of data sources, more complex queries can be created compared to a system that can only query its own database or a single data source. 
     Additionally, in certain embodiments, the BIM access system  136  can enable users to create, share, and access query packages. As described in greater detail below, a query package can encapsulate one or more pre-defined queries, one or more visualizations of queried data, and other package attributes. When a user selects a query package, the query package can be executed in a determined manner in similar fashion to other queries. As an additional advantage, in some embodiments, because the data classification system  134  can use heuristics and probabilistic algorithms to develop and modify data classifications over time, user queries are not limited to a set of predefined search variables. The BIM access system  136  is described in more detail below with reference to  FIG. 3 . 
     Example Implementation of a BIM System 
       FIG. 2  illustrates an embodiment of an implementation of the BIM system  130 . As previously described above, the BIM system  130  can include a data collection system  132  configured to, among other things, collect data from the internal data sources  120  and/or the external data sources  122 . The data collection system  132  can include a collection engine  202 , an access manager  204 , a business logic engine  206 , and a business logic security manager  208 . 
     Generally, the collection engine  202  may access the internal data sources  120  thereby providing the BIM system  130  with access to data that is stored by or generated by the internal data sources  120 . This data can include any data that may be created, accessed, or received by a user or in response to the actions of a user who is associated with the computing environment  102 . Further, in some embodiments, the collection engine  202  can access the external data sources  122  thereby providing the BIM system  130  with access to data from the external data sources  122 . In some embodiments, the data can include metadata. For example, supposing that the collection engine  202  accesses a file server, the data can include metadata associated with the files stored on the file server, such as the file name, file author, file owner, time created, last time edited, etc. 
     In some cases, a number of internal data sources  120  and/or external data sources  122  may require a user or system to be identified and/or authenticated before access to the data source is granted. Authentication may be required for a number of reasons. For example, the data source may provide individual accounts to users, such as a social networking account, email account, or collaboration system account. As another example, the data source may provide different features based on the authorization level of a user. For example, a billing system may be configured to allow all employees of an organization to view invoices, but to only allow employees of the accounting department to modify invoices. 
     For data sources that require authentication or identification of a specific user, the access manager  204  can facilitate access to the data sources. The access manager  204  can manage and control credentials for accessing the data sources. For example, the access manager  204  can store and manage user names, passwords, account identifiers, certificates, tokens, and any other information that can be used to access accounts associated with one or more internal data sources  120  and/or external data sources  122 . For instance, the access manager  204  may have access to credentials associated with a business&#39;s Facebook™ or Twitter™ account. As another example, the access manager may have access to credentials associated with an LDAP directory, a file management system, or employee work email accounts. 
     In some embodiments, the access manager  204  may have credentials or authentication information associated with a master or super user account enabling access to some or all of the user accounts without requiring credentials or authentication information associated with each of the users. In some cases, the collection engine  202  can use the access manager  204  to facilitate accessing internal data sources  120  and/or external data sources  122 . 
     The business logic engine  206  can include any system that can modify or transform the data collected by the collection engine  202  into a standardized format. In some embodiments, the standardized format may differ based on the data source accessed and/or the type of data accessed. For example, the business logic engine  206  may format data associated with emails, data associated with files stored at the computing environment  102 , data associated with web pages, and data associated with research files differently. However, each type of data may be formatted consistently. Thus, for example, data associated with product design files may be transformed or abstracted into a common format regardless of whether the product design files are of the same type. As a second example, suppose that the business logic engine  206  is configured to record time using a 24-hour clock format. In this second example, if one email application records the time an email was sent using a 24-hour clock format, and a second email application uses a 12-hour clock format, the business logic engine  206  may reformat the data from the second email application to use a 24-hour clock format 
     In some embodiments, a user may define the format for processing and storing different types of data. In other embodiments, the business logic engine  206  may identify a standard format to use for each type of data based on, for example, the format that is most common among similar types of data sources, the format that reduces the size of the information, or any other basis that can be used to decide a data format. 
     The business logic security manager  208  can include any system that can implement security and data access policies for data accessed by the collection engine  202 . In some embodiments, the business logic security manager  208  may apply the security and data access policies to data before the data is collected as part of a determination of whether to collect particular data. For example, an organization may designate a private folder or directory for each employee and the data access policies may include a policy to not access any files or data stored in the private directory. Alternatively, or in addition, the business logic security manager  208  may apply the security and data access policies to data after it is collected by the collection engine  202 . Further, in some cases, the business logic security manager  208  may apply the security and data access policies to the abstracted and/or reformatted data produced by the business logic engine  206 . For example, suppose the organization associated with the computing environment  102  has adopted a policy of not collecting emails designated as personal. In this example, the business logic security manager  208  may examine email to determine whether it is addressed to an email address designated as personal (e.g., email addressed to family members) and if the email is identified as personal, the email may be discarded by the data collection system  132  or not processed any further by the BIM system  130 . 
     In some embodiments, the business logic security manager  208  may apply a set of security and data access policies to any data or metadata provided to the classification system  134  for processing and storage. These security and data access policies can include any policy for regulating the storage and access of data obtained or generated by the data collection system  132 . For example, the security and data access policies may identify the users who can access the data provided to the data classification system  134 . The determination of which users can access the data may be based on the type of data. The business logic security manager  208  may tag the data with an identity of the users, or class or role of users (e.g., mid-level managers and more senior) who can access the data. As another example, of a security and data access policy, the business logic security manager  208  may determine how long the data can be stored by the data classification system  134  based on, for example, the type of data or the source of the data. 
     After the data collection system  132  has collected and, in some cases, processed the data obtained from the internal data sources  120  and/or the external data sources  122 , the data may be provided to the data classification system  134  for further processing and storage. The data classification system  134  can include a data repository engine  222 , a task scheduler  224 , an a priori classification engine  226 , an a posteriori classification engine  228 , a heuristics engine  230  and a set of databases  232 . 
     The data repository engine  222  can include any system for storing and indexing the data received from the data collection system  132 . The data repository engine  222  can store the data, including any generated indexes, at the set of databases  232 , which can include one or more databases or repositories for storing data. In some cases, the set of databases  232  can store data in separate databases based on any factor including, for example, the type of data, the source of data, or the security level or authorization class associated with the data and the class of users who can access the data. 
     In some implementations, the set of databases  232  can dynamically expand and, in some cases, the set of databases  232  may be dynamically structured. For example, if the data repository engine  222  receives a new type of data that includes metadata fields not supported by the existing databases of the set of databases  232 , the data repository engine  222  can create and initialize a new database that includes the metadata fields as part of the set of databases  232 . For instance, suppose the organization associated with the computing environment  102  creates its first social media account for the organization to expand its marketing initiatives. Although the databases  232  may have fields for customer information and vendor information, it may not have a field identifying whether a customer or vendor has indicated they “like” or “follow” the organization on its social media page. The data repository engine  222  can create a new field in the databases  232  to store this information and/or create a new database to capture information extracted from the social media account including information that relates to the organization&#39;s customers and vendors. 
     In certain embodiments, the data repository engine  222  can create abstractions of and/or classify the data received from the data collection system  132  using, for example, the task scheduler  224 , the a priori classification engine  226 , the a posteriori classification engine  228 , and the heuristics engine  230 . The task scheduler  224  can include any system that can manage the abstraction and classification of the data received from the data collection system  132 . In some embodiments, the task scheduler  224  can be included as part of the data repository engine  222 . 
     Data that is to be classified and/or abstracted can be supplied to the task scheduler  224 . The task scheduler  224  can supply the data to the a priori classification engine  226 , which can include any system that can classify data based on a set of user-defined, predefined, or predetermined classifications. These classifications may be provided by a user (e.g., an administrator) or may be provided by the developer of the BIM system  130 . Although not limited as such, the predetermined classifications generally include objective classifications that can be determined based on attributes associated with the data. For example, the a priori classification engine  226  can classify communications based on whether the communication is an email, an instant message, or a voice mail. As a second example, files may be classified based on the file type, such as whether the file is a drawing file (e.g., an AutoCAD™ file), a presentation file (e.g., a PowerPoint™ file), a spreadsheet (e.g., an Excel™ file), a word processing file (e.g., a Word™ file), etc. Although not limited as such, the a priori classification engine  226  generally classifies data at or substantially near the time of collection by the collection engine  202 . The a priori classification engine  226  can classify the data prior to the data being stored in the databases  232 . However, in some cases, the data may be stored prior to or simultaneously with the a priori classification engine  226  classifying the data. The data may be classified based on one or more characteristics or pieces of metadata associated with the data. For example, an email may be classified based on the email address, a domain or provider associated with the email (e.g., a Yahoo® email address or a corporate email address), or the recipient of the email. 
     In addition to, or instead of, using the a priori classification engine  226 , the task scheduler  224  can provide the data to the a posteriori classification engine  228  for classification or further classification. The a posteriori classification engine  228  can include any system that can determine trends with respect to the collected data. Although not limited as such, the a posteriori classification engine  228  generally classifies data after the data has been collected and stored at the databases  232 . However, in some cases, the a posteriori classification engine  228  can also be used to classify data as it is collected by the collection engine  202 . Data may be processed and classified or reclassified multiple times by the a posteriori classification engine  228 . In some cases, the classification and reclassification of the data occurs on a continuing basis. In other cases, the classification and reclassification of data occurs during specific time periods of events. For example, data may be reclassified each day at midnight or once a week. As another example, data may be reclassified each time one or more of the a posteriori algorithms is modified or after the collection of new data. 
     In some cases, the a posteriori classification engine  228  classifies data based on one or more probabilistic algorithms. The probabilistic algorithms may be based on any type of statistical analysis of the collected data. For example, the probabilistic algorithms may be based on Bayesian analysis or probabilities. Further, Bayesian inferences may be used to update the probability estimates calculated by the a posteriori classification engine  228 . In some implementations, the a posteriori classification engine  228  may use machine learning techniques to optimize or update the a posteriori algorithms. In some embodiments, some of the a posteriori algorithms may determine the probability that a piece or set of data (e.g., an email) should have a particular classification based on an analysis of the data as a whole. Alternatively, or in addition, some of the a posteriori algorithms may determine the probability that a set of data should have a particular classification based on the combination of probabilistic determinations associated with subsets of the data, parameters, or metadata associated with the data (e.g., classifications associated with the content of the email, the recipient of the email, the sender of the email, etc.). 
     For example, continuing with the email example, one probabilistic algorithm may be based on the combination of the classification or determination of four characteristics associated with the email, which may be used to determine whether to classify the email as a personal email, or non-work related. The first characteristic can include the probability that an email address associated with a participant (e.g., sender, recipient, BCC recipient, etc.) of the email conversation is used by a single employee. This determination may be based on the email address itself (e.g., topic based versus name based email address), the creator of the email address, or any other factor that can be used to determine whether an email address is shared or associated with a particular individual. The second characteristic can include the probability that keywords within the email are not associated with peer-to-peer or work-related communications. For example, terms of endearment and discussion of children and children&#39;s activities are less likely to be included in work related communications. The third characteristic can include the probability that the email address is associated with a participant domain or public service provider (e.g., Yahoo® email or Google® email) as opposed to a corporate or work email account. The fourth characteristic can include determining the probability that the message or email thread can be classified as conversational as opposed to, for example, formal. For example, a series of quick questions in a thread of emails, the use of a number of slang words, or excessive typographical errors may indicate that an email is likely conversational. The a posteriori classification engine  228  can use the determined probabilities for the above four characteristics to determine the probability that the email communication is personal as opposed to, for example, work-related, or spam email. 
     The combination of probabilities may not total 100%. Further, the combination may itself be a probability and the classification can be based on a threshold determination. For example, the threshold may be set such that an email is classified as personal if there is a 90% probability for three of the four above parameters indicating the email is personal (e.g., email address is used by a single employee, the keywords are not typical of peer-to-peer communication, at least some of the participant domains are from known public service providers, and the message thread is conversational). 
     As another example of the a posteriori classification engine  228  classifying data, the a posteriori classification engine  228  can use a probabilistic algorithm to determine whether a participant of an email is a customer. The a posteriori classification engine  228  can use the participant&#39;s identity (e.g., a customer) to facilitate classifying data that is associated with the participant (e.g., emails, files, etc.). To determine whether the participant should be classified as a customer, the a posteriori classification engine  228  can examiner a number of parameters including a relevant Active Directory Organizational Unit (e.g., sales, support, finance) associated with the participant and/or other participants in communication with the participant, the participant&#39;s presence in forum discussions, etc. In some cases, characteristics used to classify data may be weighted differently as part of the probabilistic algorithm. For example, email domain may be a poor characteristic to classify a participant in some cases because the email domain may be associated with multiple roles. For instance, Microsoft® may be a partner, a customer, and a competitor. 
     In some implementations, a user (e.g., an administrator) can define the probabilistic algorithms used by the a posteriori classification engine  228 . For example, suppose customer Y is a customer of business X and that the management of business X is interested in tracking the percentage of communication between business X and customer Y that relates to sales. Further, suppose that a number of employees from business X and a number of employees from business Y are in communication via email. Some of these employees may be in communication to discuss sales. However, it is also possible that some of the employees may be in communication for technical support issues, invoicing, or for personal reasons (e.g., a spouse of a business X employee may work at customer Y). Thus, in this example, to track the percentage of communication between business X and customer Y that relates to sales the user may define a probabilistic algorithm that classifies communications based on the probability that the communication relates to sales. The algorithm for determining the probability may be based on a number of pieces of metadata associated with each communication. For example, the metadata may include the sender&#39;s job title, the recipient&#39;s job title, the name of the sender, the name of the recipient, whether the communication identifies a product number or an order number, the time of communication, a set of keywords in the content of the communication, etc. 
     Using the a posteriori classification engine  228 , data may be classified based on metadata associated with the data. For example, the communication in the above example can be classified based on whether it relates to sales, supplies, project development, management, personnel, or is personal. The determination of what the data relates to can be based on any criteria. For example, the determination may be based on keywords associated with the data, the data owner, the data author, the identity or roles of users who have accessed the data, the type of data file, the size of the file, the data the file was created, etc. 
     In certain embodiments, the a posteriori classification engine  228  can use the heuristics engine  230  to facilitate classifying data. Further, in some cases, the a posteriori classification engine  228  can use the heuristics engine  230  to validate classifications, to develop probable associations between potentially related content, and to validate the associations as the data collection system  132  collects more data. In certain embodiments, the a posteriori classification engine  228  may base the classifications of data on the associations between potentially related content. In some implementations, the heuristic engine  230  may use machine learning techniques to optimize or update the heuristic algorithms. 
     In some embodiments, a user (e.g., an administrator) can verify whether the data or metadata has been correctly classified. Based on the result of this verification, in some cases, the a posteriori classification engine  228  may correct or update one or more classifications of previously processed or classified data. Further, in some implementations, the user can verify whether two or more pieces of data or metadata have been correctly associated with each other. Based on the result of this verification, the a posteriori classification engine  228  using, for example, the heuristics engine  230  can correct one or more associations between previously processed data or metadata. Further, in certain embodiments, one or more of the a posteriori classification engine  228  and the heuristics engine  230  may update one or more algorithms used for processing the data provided by the data collection system  132  based on the verifications provided by the user. 
     In some embodiments, the heuristics engine  230  may be used as a separate classification engine from the a priori classification engine  226  and the a posteriori classification engine  228 . Alternatively, the heuristics engine  230  may be used in concert with one or more of the a priori classification engine  226  and the a posteriori classification engine  228 . Similar to the a posteriori classification engine  228 , the heuristics engine  230  generally classifies data after the data has been collected and stored at the databases  232 . However, in some cases, the heuristics engine  230  can also be used to classify data as it is collected by the collection engine  202 . 
     The heuristics engine  230  can use any type of heuristic algorithm for classifying data. For example, the heuristics engine  230  can determine whether a number of characteristics are associated with the data and based on the determination, classify the data. For example, data that mentions a product, includes price information, addresses (e.g., billing and shipping addresses), and quantity information may be classified as sales data. In some cases, the heuristics engine  230  can classify data based on a subset of characteristics. For example, if a majority or two-thirds of characteristics associated with a particular classification are identified as existing in a set of data, the heuristics engine  230  can associate the classification with the set of data. In some cases, the heuristics engine  230  determines whether one or more characteristics are associated with the data. In other words, the heuristics engine can determine whether a particular characteristic is or is not associated with the data. Alternatively, or in addition, the heuristics engine  230  can determine the value or attribute of a particular characteristic associated with the data. The value or attribute of the characteristic may then be used to determine a classification for the data. For example, one characteristic that may be used to classify data is the length of the data. For instance, in some cases, a long email may make one classification more likely that a short email. 
     The a priori classification engine  226  and the a posteriori classification engine  228  can store the data classification at the databases  232 . Further, the a posteriori classification engine  228  and the heuristics engine  230  can store the probable associations between potentially related data at the databases  232 . In some cases, as classifications and associations are updated based on, for example, user verifications or updates to the a posteriori and heuristic classification and association algorithms, the data or metadata stored at the databases  232  can be modified to reflect the updates. 
     Users can communicate with the BIM system  130  using a client computing system (e.g., client  114 , client  116 , or client  118 ). In some cases, access to the BIM system  130 , or to some features of the BIM system  130 , may be restricted to users who are using clients associated with the computing environment  102 . As described above, in some cases, at least some users can access the BIM system  130  to verify classifications and associations of data by the data classification system  134 . In addition, in some cases, at least some users can access at least some of the data and/or metadata stored at the data classification system  134  using the BIM access system  136 . The BIM access system  136  can include a user interface  240 , a query manager  242 , and a query security manager  244 . 
     The user interface  240  can generally include any system that enables a user to communicate with the BIM system  130 . Further, the user interface  240  enables the user to submit a query to the BIM system  130  to access the data or metadata stored at the databases  232 . Moreover, the query can be based on any number of or type of data or metadata fields or variables. Advantageously, in certain embodiments, by enabling, a user to create a query based on any number or type of fields, complex queries can be generated. Further, because the BIM system  130  can collect and analyze data from a number of internal and external data sources, a user of the BIM system  130  can extract data that is not typically available by accessing a single data source. For example, a user can query the BIM system  130  to locate all personal messages sent by the members of the user&#39;s department within the last month. As a second example, a user can query the BIM system  130  to locate all helpdesk requests received in a specific month outside of business hours that were sent by customers from Europe. As an additional example, a product manager may create a query to examine customer reactions to a new product release or the pitfalls associated with a new marketing campaign. The query may return data that is based on a number of sources including, for example, emails received from customers or users, Facebook® posts, Twitter® feeds, forum posts, quantity of returned products, etc. 
     Further, in some cases, a user can create a relatively simple query to obtain a larger picture of an organization&#39;s knowledge compared to systems that are incapable of integrating the potentially large number of information sources used by some businesses or organizations. For example, a user can query the BIM system  130  for information associated with customer X over a time range. In response, the BIM system  130  may provide the user with all information associated with customer X over the time range, which can include who communicated with customer X, the percentage of communications relating to specific topics (e.g., sales, support, etc.), the products designed for customer X, the employees who performed any work relating to customer X and the employees&#39; roles, etc. This information may not be captured by a single source. For example, the communications may be obtained from an email server, the products may be identified from product drawings, and the employees and their roles may be identified by examining who accessed specific files in combination with the employees&#39; human resources (HR) records. 
     The query manager  242  can include any system that enables the user to create the query. The query manager  242  can cause the available types of search parameters for searching the databases  232  to be presented to a user via the user interface  240 . These search parameter types can include any type of search parameter that can be used to form a query for searching the databases  232 . For example, the search parameter types can include names (e.g., employee names, customer names, vendor names, etc.), data categories (e.g., sales, invoices, communications, designs, miscellaneous, etc.), stored data types (e.g., strings, integers, dates, times, etc.), data sources (e.g., internal data sources, external data sources, communication sources, sales department sources, product design sources, etc.), dates, etc. In some cases, the query manager  242  can also parse a query provided by a user. For example, some queries may be provided using a text-based interface or using a text-field in a Graphical User Interface (GUI). In such cases, the query manager  242  may be configured to parse the query. 
     The query manager  242  can further include any system that enables the user to create or select a query package that serves as the query. In certain embodiments, the query manager  242  can maintain query packages for each user, group of users, and/or the like. The query packages can be stored, for example, in a SQL database that maintains each user&#39;s query packages in a table by a unique identifier. In some embodiments, each user may have a profile that includes a list of package identifiers for that user. The query manager  242  can cause query packages associated with the user to be presented and made selectable via the user interface  240 . In various embodiments, the query manager  242  can also facilitate creation of new query packages. New query packages can be made accessible to users in various ways. For example, the new query packages can be created by the user, shared with the user by another user, pushed to the user by an administrator, or created in another fashion. 
     Further, the query manager  242  can cause any type of additional options for querying the databases  232  to be presented to the user via the user interface  240 . These additional options can include, for example, options relating to how query results are displayed or stored. 
     In some cases, access to the data stored in the BIM system  130  may be limited to specific users or specific roles. For example, access to the data may be limited to “Bob” or to senior managers. Further, some data may be accessible by some users, but not others. For example, sales managers may be limited to accessing information relating to sales, invoicing, and marketing, technical managers may be limited to accessing information relating to product development, design and manufacture, and executive officers may have access to both types of data, and possibly more. In certain embodiments, the query manager  242  can limit the search parameter options that are presented to a user for forming a query based on the user&#39;s identity and/or role. 
     The query security manager  244  can include any system for regulating who can access the data or subsets of data. The query security manager  244  can regulate access to the databases  232  and/or a subset of the information stored at the databases  232  based on any number and/or types of factors. For example, these factors can include a user&#39;s identity, a user&#39;s role, a source of the data, a time associated with the data (e.g., the time the data was created, a time the data was last accessed, an expiration time, etc.), whether the data is historical or current, etc. 
     Further, the query security manager  244  can regulate access to the databases  232  and/or a subset of the information stored at the databases  232  based on security restrictions or data access policies implemented by the business logic security manager  208 . For example, the business logic security manager  208  may identify all data that is “sensitive” based on a set of rules, such as whether the data mentions one or more keywords relating to an unannounced product in development. Continuing this example, the business logic security manager  208  may label the sensitive data as, for example, sensitive, and may identify which users or roles, which are associated with a set of users, can access data labeled as sensitive. The query security manager  244  can then regulate access to the data labeled as sensitive based on the user or the role associated with the user who is accessing the databases  232 . 
     Although illustrated separately, in some embodiments, the query security manager  244  can be included as part of the query manager  242 . Further, in some cases, one or both of the query security manager  244  and the query manager  242  can be included as part of the user interface  240 . In certain embodiments, some or all of the previously described systems can be combined or further divided into additional systems. Further, some or all of the previously described systems may be implemented in hardware, software, or a combination of hardware and software. 
     Example Data Collection Process 
       FIG. 3  presents a flowchart of an example of a data collection process  300 . The process  300  can be implemented by any system that can access one or more data sources to collect data for storage and analysis. For example, the process  300 , in whole or in part, can be implemented by one or more of the data collection system  132 , the collection engine  202 , the access manager  204 , the business logic engine  206 , and the business logic security manager  208 . In some cases, the process  300  can be performed generally by the BIM system  130 . Although any number of systems, in whole or in part, can implement the process  300 , to simplify discussion, the process  300  will be described in relation to specific systems or subsystems of the BIM system  130 . 
     The process  300  begins at block  302  where, for example, the collection engine  202  accesses data from the internal data sources  120 . At block  304 , the collection engine  202  accesses data from the external data sources  122 . In some cases, either the block  302  or  304  may be optional. Accessing the data may include obtaining the data or a copy of the data from the internal data sources  120 . Further, accessing the data may include accessing metadata associated with the data. In some embodiments, the collection engine  202  may obtain copies of the metadata or access the data to obtain or determine metadata associated with the data without obtaining a copy of the data. For example, in some cases, the collection engine  202  may access email from an email server to obtain metadata (e.g., sender, recipient, time sent, whether files are attached, etc.) associated with email messages with or, in some cases, without obtaining a copy of the email. 
     As previously described, accessing one or more of the internal data sources  120  and the external data sources  122  may involve using one or more credentials or accessing one or more accounts associated with the data sources. In such embodiments, the collection engine  202  may use the access manager  204  to access the credentials and/or to facilitate accessing the data sources. 
     Generally, although not necessarily, the data obtained at blocks  302  and  304  is raw data that is obtained in the format that the data is stored at the data sources with little to no modification. At block  306 , the business logic engine  206 , as described above, can reformat or transform the accessed or collected data for analysis and/or storage. Reformatting the accessed or collected data can include formatting the data to enable further processing by the BIM system  130 . Further, reformatting the accessed or collected data can include formatting the data in a format specified by a user (e.g., an administrator). In addition, in certain cases, reformatting the data can include extracting metadata from the accessed or collected data. In some cases, block  306  can include abstracting the data to facilitate analysis. For example, assuming the data under analysis is an email, a number of users may be identified. For instance, an email may include a sender, one or more recipients, which may also include users that are carbon copied, or listed on the CC line, and Blind Carbon Copied, or listed on the BCC line, and, in some cases, non-user recipients, such as lists or email addresses that result in a copy of the email being placed in an electronic folder for storage. Each of these users can be abstracted as “communication participant.” The data can then be analyzed and/or stored with each user identified, for example, as a “communication participant.” As another example of abstracting the data, the text content of each type of message can be abstracted as “message body.” Thus, an email, a Twitter® post, and a Facebook® post, and a forum post, and a product review can all be abstracted as “message body.” By abstracting data, the BIM system  130  enables more in-depth searching across multiple data sources. For example, a user can search for all messages associated with communication participant X. The result of the search can include any type of message that is associated with user X including emails sent by user X, emails received by user X, product review by user X, Twitter® posts by user X, etc. In some embodiments, the databases  232  may store the abstracted or transformed data and the original data or references to the original sources of data. In other embodiments, the databases  232  may store the abstracted or transformed data in place of the original data. 
     In some cases, reformatting the data may be optional. For example, in cases where the collection engine  202  collects metadata from sources that share a common or substantially similar data storage format, the block  306  may be unnecessary. 
     At block  308 , the business logic security manager  208  applies a security or data access policy to the collected data. Applying the security policy can include preventing the collection engine  202  from accessing some data. For example, applying the security policy can include preventing the collection engine  202  from accessing encrypted files, files associated with a specific project or user, or files marked private. Further, applying the security policy can include marking or identifying data, based on the security policy, that should not be stored at the databases  232 , should be accessible by a set of users or roles, or should be inaccessible by a set of users or roles. The business logic security manager  208  can filter any data marked for exclusion from storage in the databases  232  at block  310 . Further, the business logic security manager  208  and/or the business logic engine  206  can filter out any data to be excluded based on a data access policy, which can be based on any type of factor for excluding data. For example, data may be filtered based on the age of the data, such as files created more than five years ago or emails more than two years old. 
     At block  312 , the business logic engine  206  or the business logic security manager  208  may classify the collected and/or filtered data. The data may be classified based on, for example, who can access the data, the type of data, the source of the data, or any other factor that can be used to classify data. In some embodiments, the data may be provided to the data classification system  134  for classification. Some non-limiting embodiments of a process for classifying the data are described in further detail below with respect to the process  400 , which is illustrated in  FIG. 4 . 
     The business logic engine  206  further formats the data for storage at block  314 . Formatting the data for storage can include creating a low-level abstraction of the data, transforming the data, or extracting metadata for storage in place of the data. In some cases, block  314  can include some or all of the embodiments described above with respect to the block  306 . In some embodiments, data may go through one abstraction or transformation process at the block  306  to optimize the data for analysis and go through another abstraction or transformation process at the block  314  to optimize the data for storage and/or query access. In some embodiments, the metadata may be stored in addition to the data. Further, the metadata, in some cases, may be used for querying the databases  232 . For example, a user can search the databases  232  for information based on one or more metadata fields. In some embodiments, one or more of the blocks  306  and  314  may be optional. 
     At block  316 , the data collection system  132  can cause the data to be stored at, for example, the databases  232 . This stored data can include one or more of the collected data, the metadata, and the abstracted data. In some embodiments, storing the data can include providing the data to the data repository  222  for indexing. In such embodiments, the data repository  222  can store the indexed data at the databases  232 . 
     Although the process  300  was presented above in a specific order, it is possible for the operations of the process  300  to be performed in a different order or in parallel. For example, the business logic security manager  208  may perform the block  308 , at least in part, prior to or in parallel with the blocks  302  and  304 . As a second example, the business logic engine  206  may perform the block  306  as each item of data is accessed or after a set of data is accessed at the blocks  302  and  304 . 
     Example Data Classification Process 
       FIG. 4  presents a flowchart of an example of a data classification process  400 . The process  400  can be implemented by any system that can classify data and/or metadata. For example, the process  400 , in whole or in part, can be implemented by one or more of the data classification system  134 , the data repository engine  222 , the task scheduler  224 , the a priori classification engine  226 , the a posteriori classification engine  228 , and the heuristics engine  230 . In some cases, the process  400  can be performed generally by the BIM system  130 . Although any number of systems, in whole or in part, can implement the process  400 , to simplify discussion, the process  400  will be described in relation to specific systems or subsystems of the BIM system  130 . 
     The process  400  begins at block  402  where, for example, the data collection system  132  accesses data from one or more of the internal data sources  120  and the external data sources  122 . The data collection system  132  may use the collection engine  202  to access the data. Further, the block  402  can include some or all of the embodiments described above with respect to the blocks  302  and  304 . Moreover, some or all of the process  300  described above can be performed as part of the process performed at block  402 . In some embodiments, the process  400  can be performed as part of the block  312  above. In such embodiments, the block  402  may include the data collection system  132  providing the data, a reformatted version of the data, an abstraction of the data, and/or metadata to the data classification system  134 . In some implementations, the process  400  may be performed separately or independently of the data collection process. In such embodiments, the block  402  may include accessing the data from the databases  232 . In some cases, the databases  232  may include a database for classified data and a separate database for data that has not yet been classified. 
     At block  404 , the a priori classification engine  226  classifies the data based on a set of user-specified classification rules. As previously mentioned, a developer of the BIM system  130  or a user (e.g., an administrator) may specify the classification rules. Further, the classification rules can include any rules for classifying data based on the data or metadata associated with the data. For example, data may be classified based on the author of the data, the owner of the data, the time the data was created, etc. 
     At block  406 , the a posteriori classification engine  228  classifies the data using a posteriori analysis. This may include the a posteriori classification engine  228  using one or more probabilistic algorithms to determine one or more classifications for the data. The a posteriori classification engine  228  can use any type of probabilistic algorithm for classifying the data. For example, the classification may be based on one or more Bayesian probability algorithms. As another example, the a posteriori classification may be based on clustering of similar or dissimilar pieces of data. One example of such an approach that can be adapted for use herein is the Braun-Blanquet method that is sometimes used in vegetation science. One or both of the a priori classification and the a posteriori classification may be based on one or more variables or criteria associated with the data or metadata. 
     In some embodiments, the a posteriori classification engine  228  may use the heuristics engine  230  to facilitate calculating the probabilistic classifications of the data. For example, the a posteriori classification engine  228  can modify the probabilities used to classify the data based on a determination of the heuristics engine  230  of the accuracy of the classification of previously classified data. The heuristics engine  230  may determine the accuracy of the classification of previously classified data based on, for example, feedback by the user. This feedback may include, for example, manual reclassification of data, indications by a user of the accuracy of prior classifications, indications of the accuracy or usefulness of query results from querying the databases  232  that include the classified data, etc. Further, the heuristics engine  230  may determine the accuracy of the classification of previously classified data based on, for example, the classifications of data accessed more recently than the previously classified data. In some cases, the more recent data may have been accessed before or at the same time as the previously classified data, but may be classified after the previously classified data. 
     At block  408 , the heuristics engine  230  can classify data using a heuristics analysis. As previously described, in some cases, the heuristics engine  230  can classify the data based on the number or percentage of characteristics or attributes associated with the data that match a particular classification. 
     In some embodiments, the task scheduler  224  schedules one or more of the blocks  404 ,  406 , and  408 . Further, in some cases, the task scheduler  224  may determine whether to perform the process  400  and/or one or more of the blocks  404 ,  406 , and  408 . In some cases, one or more of the blocks  404 ,  406 , and  408  may be optional. For instance, an initial classification may be associated with data when it is collected via the process associated with the block  404 . The data may then be further classified or reclassified at collection, or at a later time, using the process associated with the block  406 , the block  408 , or a combination of the blocks  406  and  408 . 
     At block  410 , the data repository engine  222  stores or causes to be stored the data and the data classifications at the databases  232 . In some cases, the data repository engine  222  may store metadata associated with the data at the databases  232  instead of, or in addition to, storing the data. 
     At block  412 , the data repository engine  222  can update the a posteriori algorithms based on the classifications determined for the data. In addition, or alternatively, the a posteriori algorithms may be updated based on previously classified data. The a posteriori algorithms may be updated based on customer feedback and/or the determination of the heuristics engine  230  as described above with respect to the block  406 . Further, updating the a posteriori algorithms may include modifying the probabilistic weights applied to one or more variables or pieces of metadata used to determine the one or more classifications of the data. Moreover, updating the a posteriori algorithms may include modifying the one or more variables or pieces of metadata used to determine the one or more classifications of the data. In some cases, the block  412  can include modifying the heuristic algorithms used at the block  408 . For example, the number of characteristics required to classify the data with a particular classification may be modified. In addition, or alternatively, the weight applied to each of the characteristics may be modified at the block  412 . 
     As with the process  300 , it is possible for the operations of the process  400  to be performed in a different order or in parallel. For example, the blocks  404  and  406  may be performed in a different order or in parallel. 
     Example Data Query Process Using User-Provided Query 
       FIG. 5  presents a flowchart of an example of a data query process  500 . The process  500  can be implemented by any system that can process a query provided by a user or another system and cause the results of the query to be presented to the user or provided to the other system. For example, the process  500 , in whole or in part, can be implemented by one or more of the BIM access system  136 , the user interface  240 , the query manager  242 , and the query security manager  244 . In some cases, the process  500  can be performed generally by the BIM system  130 . Although any number of systems, in whole or in part, can implement the process  500 , to simplify discussion, the process  500  will be described in relation to specific systems or subsystems of the BIM system  130 . 
     The process  500  begins at block  502  where, for example, the user interface  240  receives a set of one or more search parameters from a user via a client (e.g., the client  114 ). In some embodiments, the search parameters may be provided by another computing system. For example, in some embodiments, an application running on a server (not shown) or a client (e.g., the client  116 ) may be configured to query the BIM system  130  in response to an event or at a predetermined time. The application can then use the result of the query to perform an application-specific process. For instance, an application or script may be configured to query the BIM system  130  every month to determine the workload of each employee or of the employees in a specific department of an organization to determine, for example, whether additional employees are needed or whether the allocation of human resources within different departments should be redistributed. In this example, the application can determine whether to alert a user based on the result of the determination. 
     In some implementations, a user can provide a text-based query to the user interface  240 . This text-based query can be parsed by, for example, the user interface  240  and/or the query manager  242 . Alternatively, or in addition, the user interface  240  can provide a set of query options and/or fields that a user can use to formulate a query of the BIM system  130 . The query options or fields can include any type of option or field that can be used to form a query of the BIM system  130 . For example, the query options or fields can include tags, classifications, time ranges, keywords, user identifiers, user roles, customer identifiers, vendor identifiers, corporate locations, geographic locations, etc. In some embodiments, the query options and/or search fields presented to a user may be generated based on the data stored in the databases  232 . For example, if the databases  232  includes email data, a sender field and a recipient field may be available for generating a query. However, if the databases  232  lacks any email data, the sender and recipient fields may not be available for generating a query. 
     In some cases, the query security manager  244  can limit or determine the fields or options that the user interface  240  can present to the user based on, for example, the user&#39;s permissions or the user&#39;s role. For example, fields relating to querying the BIM system  130  regarding the content of a business&#39;s email may be unavailable to a user who is not authorized to search the contents of collected email. For instance, searching the content of emails may be limited to the legal department for compliance purposes. Other users may be prohibited from searching the email content for privacy reasons. 
     At block  504 , the query manager  242  formats a query based on the search parameters received at block  502 . Formatting the query may include transforming the search parameters and query options provided by the user into a form that can be processed by the data repository engine  222 . In certain embodiments, the block  504  may be optional. For example, in some cases the search parameters may be provided by the user in a form of a query that can be processed by the BIM system  130  without modification. 
     At block  506 , the user interface  240  receives one or more user credentials from the user. In some cases, the user credentials may be received from an application. The user credentials can include any type of credential or identifier that can be used to identify a user and/or determine a set of permissions or a level of authorization associated with the user. At block  508 , the query security manager  244  can validate the user, or application, based at least in part on the user credentials received at the user interface  240 . Validating the user can include identifying the user, identifying permissions associated with the user, the user&#39;s role, and/or an authorization level associated with the user. In some embodiments, if the query security manager  244  is unable to validate the user or determines that the user lacks authorization to access the BIM system  130  and/or query the databases  232 , the query security manager  244  may reject the user&#39;s query. Further, the user interface  240  may inform the user that the user is not authorized to access the BIM system  130  or to query the databases  232 . In some implementations, if the user identifies as a guest or if the query security manager  244  is unable to validate the guest, the user may be associated with a guest identity and/or a set of guest permissions, which may permit limited access to the BIM system  130  or the data stored at the databases  232 . In some cases, a guest may receive full access to the BIM system  130 . However, the actions of the guest may be logged or logged differently than the actions of an identified user. 
     At block  510 , the query security manager  244  attaches the user permissions to the query. Alternatively, or in addition, the query security manager may attach the user&#39;s identity, role, and/or authorization level to the query. In some embodiments, one or more of the blocks  506 ,  508 , and  510  may be optional. 
     At block  512 , the query manager  242  retrieves data, and/or metadata, satisfying the query. In some implementations, the block  512  may include providing the query to the data repository engine  222  for processing. The data repository engine  222  can then query the databases  232  to obtain data that satisfies the query. This data can then be provided to the query manager  242 . 
     At decision block  514 , the query security manager  244  can determine whether the user has permission, or is authorized, to access the data that satisfies the query. Determining whether the user has permission to access the data may be based on any type of factor that can be used to determine whether a user can access data. For example, the determination may be based, at least in part, on the user&#39;s credentials, the user&#39;s permissions, a security level associated with the data, etc. In some cases, the data repository engine  222  may perform the decision block  514  as part of the process associated with the block  512 . 
     If the query security manager  244  determines that the user does not have permission to access the data, the query security manager  244  rejects the user query at block  516 . In some cases, rejecting the user query may include informing the user that the query is not authorized and/or that the user is not authorized to access the data associated with the query. In other cases, rejecting the user query may include doing nothing or presenting an indication to the user that no data satisfies the user&#39;s query. 
     If the query security manager  244  determines that the user does have permission to access the data, the user interface  240  provides the user with access to the data at block  518 . Providing the user with access to the data can include presenting the data on a webpage, in an application-generated window, in a file, in an email, or any other method for providing data to a user. In some cases, the data may be copied to a file and the user may be informed that the data is ready for access by, for example, providing the user with a copy of the file, a link to the file, or a location associated with the file. 
     With some queries, a user may be authorized to access some data that satisfies the query, but not other data that satisfies the query. In such cases, the user may be presented with the data that the user is authorized to access. Further, the user may be informed that additional data exists that was not provided because, for example, the user was not authorized to access the data. In other cases, the user may not be informed that additional data exists that was not provided. 
     In some embodiments, the decision block  514  and block  516  may be optional. For example, in some cases where the search parameters available to a user are based on the user&#39;s permissions, decision block  514  may be superfluous. However, in other embodiments, both the search parameters available to the user and the data the user can access are independently determined based on the user&#39;s permissions. 
     Advantageously, in certain embodiments, the process  500  can be used to identify new information and/or to determine trends that would be more difficult or identify or not possible to identify based on a single data source. For example, the process  500  can be used to identify the most productive and least productive employees of an organization based on a variety of metrics. Examining a single data source may not provide this information because employees serve different roles. Further, different employees are unproductive in different ways. For example, some employees may spend time an inordinate amount of time on social networking sites or emailing friends. Other employees may procrastinate by playing games or by talking in the kitchen. Thus, examining only email use or Internet activity may not provide an accurate determination of which employees are more productive. In addition, some employees can accomplish more work in less time than other employees. Thus, to determine which employees are the most productive during working hours requires examining a number of data sources. The BIM system  130  makes this possible by enabling a user to generate a query that relates the amount of time in the office to the amount of time spent procrastinating at different types of activities to the number of work-related tasks that are accomplished. 
     As a second example, the BIM system  130  can be used to identify the salespersons and the communications techniques that are most effective for each customer. For instance, a user can generate a query that relates sales, the method of communication, the content of communication, the salespersons contacting each of the customers, and the customers. Based on the result of this query, a manager may be able to determine that certain salespersons generate larger sales when using a particular communication method with a particular customer while other salespersons may be more effective with a different communication method with the particular customer or may be more effective with other customers. 
     An additional example of an application of the BIM system  130  can include gauging employee reaction to an executive memorandum or a reorganization announcement. Queries can be generated to access all communications associated with the memorandum or announcement. Alternatively, or in addition, queries can be generated to identify the general mood of employees post memorandum or announcement. These queries can examine the tone of emails and other communications (e.g., social networking posts, etc.). Additional examples of applications for using the BIM system  130  can include determining whether employees are communicating with external sources in a manner that adheres to corporate policies, communicating with customers in a timely fashion, or accessing data that is unrelated to their job role. 
     Example of a Heuristics Engine 
       FIG. 6  illustrates an example of a heuristics engine  602 . In a typical embodiment, the heuristics engine  602  operates as described with respect to the heuristics engine  230  of  FIG. 2 . In a typical embodiment, the heuristics engine  602  is operable to perform a heuristics analysis for each of a plurality of different classifications and thereby reach a classification result for each classification. The classification result may be, for example, an indication whether a given classification should be assigned to given data. For purposes of simplicity, the heuristics engine  602  may be periodically described, by way of example, with respect to a single classification. 
     The heuristics engine  602  includes a profiling engine  604  and a comparison engine  606 . In a typical embodiment, the profiling engine  604  is operable to develop one or more profiles  608  by performing, for example, a multivariate analysis. For example, in certain embodiments, the one or more profiles  608  may relate to what constitutes a personal message. In these embodiments, the profiling engine  604  can perform a multivariate analysis of communications known to be personal messages in order to develop the one or more profiles  608 . In some embodiments, the one or more profiles  608  can also be manually established. 
     In typical embodiment, the one or more profiles  608  can each include an inclusion list  610  and a filter list  612 . The inclusion list  610  can include a list of tokens such as, for example, words, that have been determined to be associated with the classification to which the profile corresponds (e.g., personal message, business message, etc.). In a typical embodiment, for each token in the inclusion list  610 , the appearance of the token in a communication makes it more likely that the communication should be assigned the classification. The filter list  612  can include a list of tokens such as, for example, words, that have been determined to have little to no bearing on whether a given communication should be assigned the classification. In some embodiments, the filter list  612  may be common across all classifications. 
     In certain embodiments, the inclusion list  610  may be associated with statistical data that is maintained by the profiling engine  604 . Based on the statistical data, the one or more profiles  608  can provide means, or expected values, relative to the inclusion list  610 . In some embodiments, the expected value may be based on an input such as a length of a given communication (e.g., a number of characters or words). According to this example, the expected value may be an expected number of “hits” on the inclusion list  610  for a personal message of a particular length. The particular length may correspond to a length of the given communication. By way of further example, the expected value may be an expected percentage of words of a personal message that are “hits” on the inclusion list  610 . Optionally, the expected percentage may be based on a length of the given communication in similar fashion to that described above with respect to the expected number of “hits.” 
     The comparison engine  606  is operable to compare data to the one or more profiles  108  based on configurations  612 . The configurations  612  typically include heuristics for establishing whether data should be classified into the classification. In particular, the configurations  612  can include one or more thresholds that are established relative to the statistical data maintained by the profiling engine  604 . For example, each threshold can be established as a number of standard deviations relative to an expected value. 
     For example, continuing the personal-message classification example described above, the configurations  614  may require that an actual value of a given metric for a new communication not be more than two standard deviations below the expected value of the given metric. In this fashion, if the actual value is not more than two standard deviations below the expected value, the new communication may be assigned the classification. The given metric may be, for example, a number or percentage of “hits” as described above. 
     Example of a Heuristics Process 
       FIG. 7  presents a flowchart of an example of a heuristics process  700  for classifying data into a classification. The process  700  can be implemented by any system that can classify data and/or metadata. For example, the process  700 , in whole or in part, can be implemented by a heuristics engine such as, for example, the heuristics engine  230  of  FIG. 2  or the heuristics engine  602  of  FIG. 6 . In some cases, the process  700  can be performed generally by the BIM system  130 . Although any number of systems, in whole or in part, can implement the process  700 , to simplify discussion, the process  700  will be described in relation to the heuristics engine. The process  700  begins at step  702 . 
     At step  702 , the heuristics engine receives new data. The new data may be considered to be representative of any data, inclusive of metadata, for which classification is desired. The new data may be, for example, a new communication. From step  702 , the process  700  proceeds to step  704 . At step  704 , the heuristics engine identifies one or more comparison attributes in the new data. For example, the one or more comparison attributes may be actual values for given metrics such as, for example, a number or percentage of “hits” on an inclusion list such as the inclusion list  610  of  FIG. 6 . From step  704 , the process  700  proceeds to step  706 . 
     At step  706 , the heuristics engine compares the one or more comparison attributes with one or more thresholds. The one or more thresholds may be defined as part of configurations such as, for example, the configurations  614  of  FIG. 6 . From step  706 , the process  700  proceeds to step  708 . At step  708 , the heuristics engine determines whether classification criteria has been satisfied. In a typical embodiment, the classification criteria is representative of criteria for determining whether the new data should be assigned the classification. The classification criteria may specify, for example, that all or a particular combination of the one or more thresholds be satisfied. 
     If it is determined at step  708  that the classification criteria not been satisfied, the process  700  proceeds to step  712  where the process  700  ends without the new data being assigned the classification. If it is determined at step  708  that the classification criteria has been satisfied, the process  700  proceeds to step  710 . At step  710 , the heuristics engine assigns the classification to the new data. From step  710 , the process  700  proceeds to step  712 . At step  712 , the process  700  ends. 
     Example of Query Packages 
     In certain embodiments, data queries as described with respect to  FIGS. 1-5  may also be accomplished using query packages. A query package generally encapsulates package attributes such as, for example, search parameters as described above with respect to queries, as long with other package attributes that enable enhanced functionality. For example, a query package can further encapsulate a package attribute that specifies a type of data visualization that is to be created using the queried data. The type of data visualization can include, for example, scatterplots, pie charts, tables, bar charts, geospatial representations, heat maps, chord charts, interactive graphs, bubble charts, candlestick charts, stoplight charts, spring graphs, and/or other types of charts, graphs, or manners of displaying data. 
     In some embodiments, query packages may run one specific query. In various other embodiments, query packages may run multiple queries. Table 1 below lists exemplary package attributes that can be included in a given query package. 
     
       
         
               
               
             
           
               
                 TABLE 1 
               
               
                   
               
               
                 PACKAGE 
                   
               
               
                 ATTRIBUTE(S) 
                 DESCRIPTION 
               
               
                   
               
             
             
               
                 Package Name 
                 A name by which the query package can be 
               
               
                   
                 referenced. 
               
               
                 Package Description 
                 A description of the query package&#39;s operation. 
               
               
                 Security Scope 
                 Optionally specify a security and data access policy 
               
               
                   
                 as described with respect to FIG. 2. 
               
               
                 Visualization 
                 Specifies a type of data visualization such as, for 
               
               
                   
                 example, scatterplots, pie charts, tables, bar charts, 
               
               
                   
                 geospatial representations, heat maps, chord charts, 
               
               
                   
                 interactive graphs, bubble charts, candlestick charts, 
               
               
                   
                 stoplight charts, spring graphs, and/or other types of 
               
               
                   
                 charts, graphs, or manners of displaying data. 
               
               
                   
                 In cases where the package is representative of 
               
               
                   
                 multiple queries, the visualization attribute may be 
               
               
                   
                 represented as an array of visualizations that can 
               
               
                   
                 each have a visualization type, a data source, and a 
               
               
                   
                 target entity (e.g., entity that is being counted such 
               
               
                   
                 as, for example, messages, message participants, 
               
               
                   
                 etc.) 
               
               
                 Default Group-By 
                 Retrieves data according to, for example, one or 
               
               
                 Field 
                 more data columns (e.g., by location, department, 
               
               
                   
                 etc.). 
               
               
                 Aggregation Period 
                 A time period such as, for example, daily, hourly, 
               
               
                   
                 etc. 
               
               
                 Data-Smoothing 
                 Specifies one or more algorithms that attempt to 
               
               
                 Attributes 
                 capture important patterns in the data, while leaving 
               
               
                   
                 out noise or other fine-scale structures/rapid 
               
               
                   
                 phenomena. 
               
               
                 Visualization-Specific 
                 Certain types of visualizations may require 
               
               
                 Attributes 
                 additional attributes such as, for example, 
               
               
                   
                 specification of settings for sorting, number of 
               
               
                   
                 elements in a data series, etc. 
               
               
                 Facet Names 
                 Data (or fields) related to the query that can be used 
               
               
                   
                 to categorize data. Particular values of facets can be 
               
               
                   
                 used, for example, to constrain query results. 
               
               
                 Array of Entities 
                 An array of entities that can each have, for example, 
               
               
                   
                 a name, entity type (e.g., message), filter  
               
               
                   
                 expression, and a parent-entity property. 
               
               
                 Array of Facets 
                 An array of facets that can each have, for example, a 
               
               
                   
                 name, group-by field, and a minimum/maximum 
               
               
                   
                 number of results to show. 
               
               
                   
               
             
          
         
       
     
     In a typical embodiment, query packages can be shared among users or distributed to users, for example, by an administrator. In a typical embodiment, one user may share a particular query package with another user or group of users via the user interface  240 . In similar fashion the other user or group of users can accept the query package via the user interface  240 . Therefore, the query manager  242  can add the shared query package for the user or group of users. As described above, the query manager  242  generally maintains each user&#39;s query packages in a table by a unique identifier. In a typical embodiment, query packages further facilitate sharing by specifying data and data sources in a relative fashion that is, for example, relative to a user running the query. For example, package attributes can refer to data owned by a user running the query or to data that is owned by users under the supervision of the user running the query rather than to specific data or users. 
     Example Data Query Process Using Query Packages 
       FIG. 8  presents a flowchart of an example of a data query process  800  that uses query packages. The process  800  can be implemented by any system that can process a query package provided by a user or another system and cause the results of a query encapsulated therein to be presented to the user or provided to the other system. For example, the process  800 , in whole or in part, can be implemented by one or more of the BIM access system  136 , the user interface  240 , the query manager  242 , and the query security manager  244 . In some cases, the process  800  can be performed generally by the BIM system  130 . Although any number of systems, in whole or in part, can implement the process  800 , to simplify discussion, the process  800  will be described in relation to specific systems or subsystems of the BIM system  130 . 
     The process  800  begins at block  802  where, for example, the user interface  240  from a user a selection of a query package. In various embodiments, the query package may be selected from a list or graphical representation of query packages. As described above, the query package typically specifies a data visualization based on a data query. In various embodiments, the query package may specify more than one data visualization and/or be based on more than one data query. At block  804 , the query manager  242  formats one or more queries based on the query package selected at block  802 . In certain embodiments, the block  804  may be optional. For example, in some cases the query package may already include a query that can be processed by the BIM system  130  without modification. 
     At block  806 , the user interface  240  receives one or more user credentials from the user. In some cases, the user credentials may be received from an application. The user credentials can include any type of credential or identifier that can be used to identify a user and/or determine a set of permissions or a level of authorization associated with the user. At block  808 , the query security manager  244  can validate the user, or application, based at least in part on the user credentials received at the user interface  240 . Validating the user can include identifying the user, identifying permissions associated with the user, the user&#39;s role, and/or an authorization level associated with the user. In some embodiments, if the query security manager  244  is unable to validate the user or determines that the user lacks authorization to access the BIM system  130  and/or query the databases  232 , the query security manager  244  may reject the one or more queries. Further, the user interface  240  may inform the user that the user is not authorized to access the BIM system  130  or to query the databases  232 . In some implementations, if the user identifies as a guest or if the query security manager  244  is unable to validate the guest, the user may be associated with a guest identity and/or a set of guest permissions, which may permit limited access to the BIM system  130  or the data stored at the databases  232 . In some cases, a guest may receive full access to the BIM system  130 . However, the actions of the guest may be logged or logged differently than the actions of an identified user. 
     At block  810 , the query security manager  244  attaches the user permissions to the one or more queries. Alternatively, or in addition, the query security manager may attach the user&#39;s identity, role, and/or authorization level to the one or more queries. In some embodiments, one or more of the blocks  806 ,  808 , and  810  may be optional. 
     At block  812 , the query manager  242  retrieves data, and/or metadata, satisfying the one or more queries. In some implementations, the block  812  may include providing the one or more queries to the data repository engine  222  for processing. The data repository engine  222  can then query the databases  232  to obtain data that satisfies the one or more queries. This data can then be provided to the query manager  242 . 
     At decision block  814 , the query security manager  244  can determine whether the user has permission, or is authorized, to access the data that satisfies the one or more queries. Determining whether the user has permission to access the data may be based on any type of factor that can be used to determine whether a user can access data. For example, the determination may be based, at least in part, on the user&#39;s credentials, the user&#39;s permissions, a security level associated with the data, etc. In some cases, the data repository engine  222  may perform the decision block  814  as part of the process associated with the block  812 . 
     If the query security manager  244  determines that the user does not have permission to access the data, the query security manager  244  rejects the one or more queries at block  816 . In some cases, rejecting the one or more queries may include informing the user that the query package not authorized and/or that the user is not authorized to access the data associated with the query package. In other cases, rejecting the one or more queries may include doing nothing or presenting an indication to the user that no data satisfies the query package. 
     If the query security manager  244  determines that the user does have permission to access the data, the query manager  242  (or a separate visualization component) generates the data visualization at block  818 . At block  820 , the user interface  240  provides the data visualization to the user. Providing the user the data visualization can include presenting the data visualization on a webpage, in an application-generated window, in a file, in an email, or any other method for providing data to a user. In some cases, the data visualization may be copied to a file and the user may be informed that the data visualization is ready for access by, for example, providing the user with a copy of the file, a link to the file, or a location associated with the file. 
       FIG. 9  illustrates an example of a user interface that can be used by a user to select a query package. 
       FIG. 10  illustrates an example of a user interface that can be used by a user to create or modify a query package. 
     Example of a Data Model 
     Table 2 below provides an example of a data model that can be utilized by a BIM system such as, for example, the BIM system  130 . In particular, Table 2 illustrates several entities that can be used to model communications such as, for example, personal communications or business communications. 
                             TABLE 2               ENTITY   FIELD   DATA TYPE                   Message   Body   String           Classifications   Strings           Content   String           Date   Date Time           External Recipients   Entities (Message Participant)           File Attachments   Entities (File)           In reply to   Entity (Message)           Internal Recipients   Entities (Message Participant)           Is Encrypted   Boolean           Message Attachments   Entities (Messages)           Message IDs   Strings           Original Message ID   String           Participants   Entities (Message Participant)           Platform   Enum (Message Platform type)           Recipients   Entities (Message Participant)           Send Date   Date Time           Send Time of Day   Time           Sender   Entity (Message Participant)           Size   Integer           Subject   String           Thread   Entity (Message Thread)           Type   Enum (Message Address Type)       Message   Date   Date Time       Participant   Deletion Date   Date Time           Delivery Time   Time Span           Has Been Delivered   Boolean           ID   String           Is Addressed in BCC   Boolean           Is Addressed in CC   Boolean           Is Addressed in TO   Boolean           Is External Recipient   Boolean           Is Internal Recipient   Boolean           Is Recipient   Boolean           Is Sender   Boolean           MessgeAsSender   Entity (Message)           MessageAsInternalRecipient    Entity (Message)           MessageAsExternalRecipient   Entity (Message)           Message Address   Entity (Message Address)           Person   Entity (Person Snapshot)           Receipt Date   Date Time           Receipt Time of Day   Time           Responses   Entity (Message)           Response Time   Time Span       Message   Domain   Entity (ONS Domain)       Address   Is External   Boolean           Is Internal   Boolean           Name   String           Platform   Enum (Message Platform Type)           Type   Enum (Message Address Type       DNS    Name   String       Domain   Address   Entities (Messaging Address)       Person   All Reports   Entities (Person Snapshot)       Snapshot   Company   String           Department   String           Direct Reports   Entities (Person Snapshot)           First Name   String           Full Name   String           History   Entity (Person History)           ID   String           Initials   String           Job Title   String           Last Name   String           Manager   Entity (Person Snapshot)           Managers   Entities (Person Snapshot)           Messaging Addresses    Entities (Message Address)           Message Participants               Office   String           OU   String           Snapshot Date   Date Time           Street Address   Complex Type (Street Address)           Telephone Numbers   Strings       Street    City   String       Address   Country or Region   String           PO Box   String           State or Province   String           Street   String           Zip or Postal Code   String       Person    Current   Entity (Person)       History   Historic   Entities (Person)           ID   String           Messages   Entities (Message)           Timestamp   Date Time       Message    ID   String       Thread   Messages   Entities (Message)           Participants   Entities (Message Participant           Thread subject   String           Timestamp   Date Time       File   Filename   String           ID   String           Messages   Entities (Message)           Modified Date   Date Time           Size   Integer           Hash   String                    
Examples of Utilization of a BIM Access System
 
     Table 3, Table 4, and Table 5 below provide several examples of how a BIM access system such as, for example, the BIM access system  136 , can be utilized. In various embodiments, each example may be implemented as user-generated queries or query packages as described above. In particular, Table 3 illustrates uses cases for gleaning operational insights. Table 4 illustrates use cases for gleaning business insights. Table 5 illustrates uses cases for gleaning compliance insights. 
     
       
         
               
               
               
               
               
             
           
               
                 TABLE 3 
               
               
                   
               
               
                   
                 USER 
                   
                   
                   
               
               
                 USE CASE 
                 PERSONA 
                 POTENTIAL OBJECTIVE(S) 
                 INPUT 
                 OUTPUT 
               
               
                   
               
             
             
               
                 Find Lost 
                 Helpdesk 
                 1. Help a mail user understand 
                 Sender name, 
                 Indication 
               
               
                 Message 
                 Personnel 
                 why they (or a recipient) apparently 
                 recipient name, 
                 whether message 
               
               
                 (Helpdesk) 
                   
                 didn&#39;t receive a message; 
                 message date 
                 was delivered 
               
               
                   
                   
                 2. Help that user prove whether the 
                 range, and 
                 and, if not, a 
               
               
                   
                   
                 message was delivered or not, or 
                 message subject. 
                 location of where 
               
               
                   
                   
                 whether message was caught by junk 
                   
                 message was last 
               
               
                   
                   
                 filter; and 
                   
                 located. 
               
               
                   
                   
                 3. Escalate the problem to IT if 
                   
                   
               
               
                   
                   
                 there is a system problem. 
                   
                   
               
               
                 Find Lost 
                 Mail User 
                 1 Understand why someone 
                 Sender name, 
                 Was message 
               
               
                 Message (Self- 
                   
                 apparently didn&#39;t receive a message I 
                 recipient name, 
                 delivered or is it 
               
               
                 Service) 
                   
                 sent them. 
                 message date/time, 
                 in transit 
               
               
                   
                   
                 2. Discover whether the message 
                 message subject 
                   
               
               
                   
                   
                 was actually delivered. 
                   
                   
               
               
                   
                   
                 3. Report a system problem to IT if 
                   
                   
               
               
                   
                   
                 necessary. 
                   
                   
               
               
                 Track 
                 Mail User 
                 1. Determine whether a specific 
                 Sender name, 
                 Was message sent 
               
               
                 Anticipated 
                   
                 person sent a message that was expected 
                 recipient name, 
                 and delivered or is 
               
               
                 Message 
                   
                 to be sent. 
                 message date range 
                 it in transit 
               
               
                   
                   
                 2. Determine whether the message 
                   
                   
               
               
                   
                   
                 was actually sent, or lost in transit. 
                   
                   
               
               
                 Measure 
                 IT Manager 
                 1. Track the average and maximum 
                 Source (mailbox/ 
                 Textual output of 
               
               
                 Internal Mail 
                   
                 message delivery times of internal mail 
                 site), target 
                 compliance 
               
               
                 Delivery time 
                   
                 system. 
                 (mailbox/site) 
                 results, drill-into 
               
               
                 Compliance 
                   
                   
                   
                 the “Analyze 
               
               
                   
                   
                   
                   
                 Internal Mail 
               
               
                   
                   
                   
                   
                 Delivery Times” 
               
               
                   
                   
                   
                   
                 scenario (and 
               
               
                   
                   
                   
                   
                 accompanying 
               
               
                   
                   
                   
                   
                 charts) to find out 
               
               
                   
                   
                   
                   
                 where your SLA 
               
               
                   
                   
                   
                   
                 was NOT met. 
               
               
                 Analyze 
                 Messaging 
                 1. Show and trend the delivery times 
                 Source (mailbox/ 
                 Trend charts of 
               
               
                 Internal Mail 
                 Administrator 
                 between internal servers. 
                 site), target 
                 overall, site to 
               
               
                 Delivery 
                   
                 2. Identify problem areas, or future 
                 (mailbox/site), 
                 site, or server to 
               
               
                 Times 
                   
                 problem areas, regarding inter- 
                 filter (maximum 
                 server average/ 
               
               
                   
                   
                 organization mail delivery. 
                 delivery time 
                 maximum 
               
               
                   
                   
                   
                 between 2 end- 
                 delivery times 
               
               
                   
                   
                   
                 points) 
                   
               
               
                 Diagnose Slow 
                 Messaging 
                 1. Investigate why a particular 
                 Sender, recipient, 
                 Details of 
               
               
                 or Lost 
                 Administrator 
                 message was slow to be delivered. 
                 message date/ 
                 message delivery 
               
               
                 Delivery for a 
                   
                 2. Determine whether there is a 
                 time, subject 
                 path and timing 
               
               
                 Particular 
                   
                 problem with the mail system 
                 wildcard, Filter on 
                   
               
               
                 Message 
                   
                 3. Take any necessary corrective 
                 message header 
                   
               
               
                   
                   
                 action, 
                 (including x- 
                   
               
               
                   
                   
                   
                 headers) 
                   
               
               
                 Compare and 
                 IT Manager, 
                 1. Regularly compare and trend the 
                 Date range, data 
                 Trend of relative 
               
               
                 Trend Usage 
                 Executive 
                 usage of different communications 
                 sources (Exchange, 
                 platform usage 
               
               
                 across 
                   
                 systems. 
                 Lync/OCS), users 
                 over time, point- 
               
               
                 Communication 
                   
                 2. Perform capacity planning and 
                 (department/site) 
                 in-time chart 
               
               
                 Systems 
                   
                 make infrastructure investment 
                   
                   
               
               
                   
                   
                 decisions. 
                   
                   
               
               
                   
                   
                 3. Track changes in user behavior 
                   
                   
               
               
                   
                   
                 to communication usage directives. 
                   
                   
               
               
                 Analyze Non- 
                 Messaging and 
                 1. Show point-in-time, and trending, 
                 Date time range, 
                 Table with 
               
               
                 Delivery 
                 Messaging 
                 of an aggregate number and type of 
                 target domain, site,  
                 aggregate 
               
               
                 Reports 
                 Administrator 
                 NDRs (e.g., rejected, bounced, blocked, 
                 server, sender 
                 numbers by type, 
               
               
                 (NDR&#39;s) 
                   
                 email error). 
                   
                 Charts for 
               
               
                   
                   
                 2. Detect and troubleshoot NDR 
                   
                 trending of NDRs 
               
               
                   
                   
                 issues with my messaging system, and 
                   
                 by type, Optimal: 
               
               
                   
                   
                 identify trends BIM 
                   
                 Pivot Viewer to 
               
               
                   
                   
                   
                   
                 slice- and-dice the 
               
               
                   
                   
                   
                   
                 data (which 
               
               
                   
                   
                   
                   
                 senders are 
               
               
                   
                   
                   
                   
                 generating NDR&#39;s, 
               
               
                   
                   
                   
                   
                 etc . . . to help you 
               
               
                   
                   
                   
                   
                 diagnose the 
               
               
                   
                   
                   
                   
                 problem) 
               
               
                 View List of 
                 Messaging 
                 1. Drill into the details of a message  
                 Date range, 
                 List of messages 
               
               
                 Messages 
                 Administrator, 
                 report to see a list of messages sent or  
                 mailbox, type of 
                 and corresponding 
               
               
                 Details of a 
                 Management 
                 received by a particular user. 
                 message (sent or 
                 details 
               
               
                 Message Stats 
                   
                 2. Perform light-weight auditing and 
                 received) 
                   
               
               
                 Report 
                   
                 forensics. 
                   
                   
               
               
                   
                   
                 3. Further understand the message 
                   
                   
               
               
                   
                   
                 report (e.g., what is the subject of 
                   
                   
               
               
                   
                   
                 messages going to a particular email 
                   
                   
               
               
                   
                   
                 domain). 
                   
                   
               
               
                 Ensure 
                 Messaging 
                 1. Understand who and how many  
                 “Network” 
                 Show me all 
               
               
                 Encrypted 
                 Administrator, 
                 encrypted messages are being sent on 
                 (identified by 
                 encrypted 
               
               
                 Message 
                 Management 
                 which network. 
                 domain, ip-subnet, 
                 messages that 
               
               
                 Usage 
                   
                 2. Track adherence to corporate 
                 ip-address). 
                 didn&#39;t meet the 
               
               
                   
                   
                 policy on encrypted message use. 
                 Recipient, date 
                 criteria. Volume 
               
               
                   
                   
                   
                 range. 
                 number + textual 
               
               
                   
                   
                   
                   
                 output of 
               
               
                   
                   
                   
                   
                 messages in 
               
               
                   
                   
                   
                   
                 violation 
               
               
                 Understand 
                 Messaging 
                 1. See aggregate number of messages 
                 Filter (DSN or 
                 Aggregate 
               
               
                 Connector 
                 Administrator 
                 and specific message-level details being 
                 NDR, External vs. 
                 message counts 
               
               
                 Capacity and 
                   
                 sent or received over a particular MTA, 
                 Internal), Date 
                 by connector 
               
               
                 Distribution 
                   
                 where MTA can be, for example, an 
                 time range, 
                 (chart), individual 
               
               
                   
                   
                 Exchange Server (2003 Front-End or 
                 Exchange Server 
                 message details 
               
               
                   
                   
                 2007 HUB Transport) or Exchange 
                 or Connector and 
                 (including client- 
               
               
                   
                   
                 HUB Receive Connector. 
                 Edge servers 
                 ip, client- 
               
               
                   
                   
                 2. Understand how busy the 
                   
                 hostname, server- 
               
               
                   
                   
                 connectors are and plan for over/under 
                   
                 ip, server- 
               
               
                   
                   
                 saturated connectors accordingly. 
                   
                 hostname, 
               
               
                   
                   
                 3. Report on which external 
                   
                 connector-id, 
               
               
                   
                   
                 peripheral mail servers and other 
                   
                 event-id, 
               
               
                   
                   
                 systems are sending messages over 
                   
                 recipient-address, 
               
               
                   
                   
                 which connectors. 
                   
                 total-bytes, 
               
               
                   
                   
                   
                   
                 recipient-count, 
               
               
                   
                   
                   
                   
                 sender- address, 
               
               
                   
                   
                   
                   
                 message-subject), 
               
               
                   
                   
                   
                   
                 Topology 
               
               
                   
                   
                   
                   
                 Visualization 
               
               
                 Troubleshoot 
                 Messaging 
                 1. See real-time message activity 
                 Exchange Server 
                 Aggregate 
               
               
                 Connector 
                 Administrator 
                 across connectors. 
                 or Connector and 
                 message counts 
               
               
                 Message Flow 
                   
                 2. Troubleshoot a message flow issue 
                 Edge servers, 
                 by connector 
               
               
                   
                   
                 which could be caused by either a 
                 inbound or 
                 (chart), individual 
               
               
                   
                   
                 connector issue or an external event (e.g. 
                 outbound, domain 
                 message details 
               
               
                   
                   
                 DOS attack, SPAM, looping message). 
                 or queue 
                 (including client- 
               
               
                   
                   
                   
                 (associated with 
                 ip, client- 
               
               
                   
                   
                   
                 the connector). 
                 hostname, server- 
               
               
                   
                   
                   
                   
                 ip, server- 
               
               
                   
                   
                   
                   
                 hostname, 
               
               
                   
                   
                   
                   
                 connector-id, 
               
               
                   
                   
                   
                   
                 event-id, 
               
               
                   
                   
                   
                   
                 recipient-address, 
               
               
                   
                   
                   
                   
                 total-bytes, 
               
               
                   
                   
                   
                   
                 recipient-count, 
               
               
                   
                   
                   
                   
                 sender-address, 
               
               
                   
                   
                   
                   
                 message-subject), 
               
               
                   
                   
                   
                   
                 Topology 
               
               
                   
                   
                   
                   
                 Visualization 
               
               
                 Understand 
                 IT Manager, 
                 1. Compare usage across messaging 
                 Date time range, 
                 Aggregate 
               
               
                 User Client 
                 Messaging 
                 clients (Outlook/OWA/BlackBerry/ 
                 users, groups, 
                 numbers for users 
               
               
                 Usage 
                 Administrator, 
                 ActiveSync). 
                 devices 
                 and groups, 
               
               
                   
                 Executives 
                 Understand usage of desktop vs. mobile 
                   
                 Charting, 
               
               
                   
                   
                 and justify ROI where necessary, 
                   
                 Trending, 
               
               
                   
                   
                 possible risk mobile assessment usage. 
                   
                 Comparison 
               
               
                   
                   
                 2. Determine whether people are 
                   
                 across users and 
               
               
                   
                   
                 trending towards not using desktop 
                   
                 groups, Pivot 
               
               
                   
                   
                 computers. 
                   
                 Viewer 
               
               
                 Understand 
                 Messaging 
                 1. Understand mobile (e.g., 
                 Server End-points, 
                 Overall aggregate 
               
               
                 Mobile 
                 Administrator 
                 BlackBerry, ActiveSync) usage on my 
                 Date time range, 
                 numbers for end- 
               
               
                 Infrastructure 
                   
                 messaging infrastructure Perform 
                 devices 
                 point, Trending 
               
               
                 Usage 
                   
                 capacity planning for my mobile 
                   
                   
               
               
                   
                   
                 growth 
                   
                   
               
               
                 Understand 
                 Messaging 
                 1. Find all the messages that have 
                 Date time range, 
                 Charts, pivots of 
               
               
                 Usage of 
                 Administrator 
                 originated from specific end-user mail 
                 users, specific 
                 aggregate 
               
               
                 “Special” 
                   
                 clients or servers. 
                 message header 
                 numbers, 
               
               
                 Messages 
                   
                 2. Assess risks or determine usage. 
                 information 
                 aggregate trends, 
               
               
                 (using 
                   
                 Special messages generally have 
                   
                 List of messages 
               
               
                 message 
                   
                 particular metadata in the X-Headers 
                   
                 and details, 
               
               
                 headers) 
                   
                 such as mail classification. 
                   
                 message volumes 
               
               
                   
                   
                   
                   
                 grouped by header 
               
               
                   
                   
                   
                   
                 information. 
               
               
                 Search for 
                 Messaging 
                 1. Find all the messages that have 
                 Date time range, 
                 List of messages 
               
               
                 “Special 
                 Administrator 
                 particular message header criteria 
                 major header fields 
                 and details 
               
               
                 Messages” 
                   
                 2. Discover messages sent from non- 
                 (date/time, sender, 
                   
               
               
                 (customer 
                   
                 Exchange servers and flexible specific 
                 recipient(s), 
                   
               
               
                 defined) 
                   
                 message searches. 
                 subject, etc . . . ) 
                   
               
               
                 Alert on 
                 Messaging 
                 1. Learn about abnormal message 
                 Date time range, 
                 Notification 
               
               
                 Abnormal 
                 Administrator 
                 volumes for a user, server, connector, or 
                 server/queue, 
                   
               
               
                 Message 
                   
                 queue. 
                 connector, use 
                   
               
               
                 Volume 
                   
                 2. Be alerted of a potential problem 
                   
                   
               
               
                   
                   
                 and investigate (see next scenario). 
                   
                   
               
               
                 Investigate 
                 Messaging 
                 1. Investigate a period of abnormal 
                 Date time range, 
                 Topology, list of 
               
               
                 Abnormal 
                 Administrator 
                 message volume (could be on a user, 
                 target filter (server,  
                 messages with 
               
               
                 Message 
                   
                 server, connector, or a queue). 
                 queue, user, filter) 
                 details, message 
               
               
                 Volume 
                   
                 Determine if its spam being received or 
                   
                 volumes grouped 
               
               
                   
                   
                 sent or some other problem that needs to 
                   
                 by time 
               
               
                   
                   
                 be addressed. 
                   
                   
               
               
                 Investigate 
                 Messaging 
                 1. Investigate suspicious messages 
                 Date time range 
                 List of messages 
               
               
                 Potential Spam 
                 Administrator 
                 being sent from within my organization 
                   
                 and message 
               
               
                 Messages 
                   
                 (open relay or spoofed header). Are 
                   
                 details, 
               
               
                 Originating 
                   
                 messages being sent with open relays 
                   
                 server/relay 
               
               
                 from my 
                   
                 within my organization? 
                   
                 involved, client 
               
               
                 Organization 
                   
                 2. Stop abusive behavior by users. 
                   
                 IPs 
               
               
                 View Internal 
                 IT Manager, 
                 1. Understand the load distribution of 
                 Infrastructure 
                 Topological 
               
               
                 Infrastructure 
                 Messaging 
                 my internal messaging infrastructure 
                 components (user 
                 View, Charts for 
               
               
                 Distribution 
                 Administrator 
                 components (servers, stores, 
                 defined), date 
                 trending of 
               
               
                   
                   
                 connectors). Budget for growth 
                 range 
                 messages load 
               
               
                   
                   
                 accordingly and optimize performance. 
               
               
                   
               
             
          
         
       
     
     
       
         
               
               
               
               
               
             
           
               
                 TABLE 4 
               
               
                   
               
               
                   
                 USER 
                   
                   
                   
               
               
                 USE CASE 
                 PERSONA 
                 POTENTIAL OBJECTIVE(S) 
                 INPUT 
                 OUTPUT 
               
               
                   
               
             
             
               
                 Understand 
                 Manager 
                 1. Track average and maximum 
                 List of mailboxes, 
                 Trending with 
               
               
                 User Response 
                   
                 response times of members of my 
                 AD groups, filters 
                 charts with 
               
               
                 Times 
                   
                 department (or another group) to 
                 (such as types of 
                 overall or 
               
               
                   
                   
                 “customer” messages overtime. 
                 messages, internal  
                 individual 
               
               
                   
                   
                 2. Track compliance against my 
                 vs. external, 
                 response times, 
               
               
                   
                   
                 customer SLA&#39;s. 
                 recipient 
                 list of messages 
               
               
                   
                   
                 3. Identify areas for improvement 
                 domains), date 
                 (including 
               
               
                   
                   
                 and measure performance. 
                 range 
                 message level 
               
               
                   
                   
                   
                   
                 details), Pivot 
               
               
                   
                   
                   
                   
                 Table to explore 
               
               
                 Investigate 
                 Manager, 
                 1. Review all communications 
                 Target user, types 
                 Details of all 
               
               
                 Employee 
                 Messaging 
                 between one of my employees and 
                 of messages to 
                 communications 
               
               
                 Communications 
                 Administrator 
                 another user or domain Respond to a 
                 include/exclude, 
                 by my employee 
               
               
                   
                   
                 complaint or review the usage of my 
                 date range 
                 (list of messages 
               
               
                   
                   
                 employee for HR purposes 
                   
                 and the ability to 
               
               
                   
                   
                   
                   
                 access message 
               
               
                   
                   
                   
                   
                 level details) 
               
               
                 Measure User 
                 Manager 
                 1. Track and compare the 
                 List of mailboxes 
                 Productivity 
               
               
                 Productivity 
                   
                 productivity profiles (volume of 
                 or AD groups, a 
                 report (message 
               
               
                   
                   
                 messages sent and received and the 
                 selected group of 
                 volumes and 
               
               
                   
                   
                 response times) of my employees and 
                 employees that 
                 response times) 
               
               
                   
                   
                 groups of employees. 
                 can be compared 
                 and trending, 
               
               
                   
                   
                 2. Gain insight into my employees&#39; 
                   
                 statistics such as 
               
               
                   
                   
                 time and performance as it pertains to 
                   
                 averages, pivot 
               
               
                   
                   
                 messaging usage. 
                   
                 for exploring 
               
               
                   
                   
                 3. Compare productivity from a 
                   
                   
               
               
                   
                   
                 messaging perspective of users within 
                   
                   
               
               
                   
                   
                 a group. 
                   
                   
               
               
                   
                   
                 Identify areas for improvement. 
                   
                   
               
               
                 Track After- 
                 Manager, 
                 1. Regularly review a list of 
                 Customer 
                 Text - list of 
               
               
                 Hours 
                 Administrator 
                 messages that arrive during a certain 
                 Definition of 
                 messages (with 
               
               
                 Communications 
                   
                 time of day. 
                 ‘Time of Day’, 
                 details), volume 
               
               
                   
                   
                 2. Bill my customers extra for 
                 Senders, 
                 report, ability 
               
               
                   
                   
                 after-hours support. 
                 recipients, 
                 export 
               
               
                   
                   
                 3. Audit the usage of the messaging 
                 message date 
                   
               
               
                   
                   
                 system after hours. 
                 range, time of day 
                   
               
               
                   
                   
                 4. Look at my messaging load 
                 range, message 
                   
               
               
                   
                   
                 during a specific time of day. 
                 filter defining 
                   
               
               
                   
                   
                   
                 what types 
                   
               
               
                   
                   
                   
                 messages to 
                   
               
               
                   
                   
                   
                 include (i.e. don&#39;t 
                   
               
               
                   
                   
                   
                 include SPAM 
                   
               
               
                   
                   
                   
                 messages) 
                   
               
               
                 Track Outlook 
                 Manager 
                 Report on user Outlook Category and  
                 Recipients, 
                 Aggregate ratios, 
               
               
                 Categorization 
                   
                 Flag usage. 
                 Category and/or 
                 Charts to trend of 
               
               
                 &amp; Flag 
                   
                 Measure adherence to business or 
                 Flag, Date Range, 
                 overall or 
               
               
                   
                   
                 workflow processes and directives. 
                 Message Filter 
                 individual 
               
               
                   
                   
                   
                 (type of messages 
                 Outlook category 
               
               
                   
                   
                   
                 to include) 
                 usage, trend 
               
               
                   
                   
                   
                   
                 individual 
               
               
                   
                   
                   
                   
                 Categories, 
               
               
                   
                   
                   
                   
                 ability to drill 
               
               
                   
                   
                   
                   
                 into individual 
               
               
                   
                   
                   
                   
                 messages, Pivot 
               
               
                   
                   
                   
                   
                 Table to explore 
               
               
                   
                   
                   
                   
                 the categories use 
               
               
                   
                   
                   
                   
                 among groups 
               
               
                   
                   
                   
                   
                 and individuals. 
               
               
                 Track User 
                   
                 1. Track by status of tasks (usage 
                   
                   
               
               
                 Outlook 
                   
                 number per each status available). 
                   
                   
               
               
                 Actions 
                   
                 2. Track task of attaching pictures, 
                   
                   
               
               
                   
                   
                 images and attachments to a task in 
                   
                   
               
               
                   
                   
                 Outlook. 
                   
                   
               
               
                   
                   
                 3. Track by address types and 
                   
                   
               
               
                   
                   
                 phone types (usage number per each 
                   
                   
               
               
                   
                   
                 address/phone type. 
                   
                   
               
               
                   
                   
                 4. Track Untimed tasks in Outlook 
                   
                   
               
               
                   
                   
                 (e.g., where start date and due date is 
                   
                   
               
               
                   
                   
                 equal to none. 
                   
                   
               
               
                   
                   
                 5. Determine average activities and 
                   
                   
               
               
                   
                   
                 tasks created per day. 
                   
                   
               
               
                   
                   
                 6. Ascertain the current usage of 
                   
                   
               
               
                   
                   
                 notes in Outlook. For example, can we 
                   
                   
               
               
                   
                   
                 get examples of what people are 
                   
                   
               
               
                   
                   
                 putting in the notes section? 
                   
                   
               
               
                   
                   
                 7. Track the journal capability 
                   
                   
               
               
                   
                   
                 attached to contacts in Outlook. Is 
                   
                   
               
               
                   
                   
                 anyone using this? Can we get 
                   
                   
               
               
                   
                   
                 examples of this? 
                   
                   
               
               
                 Audit 
                 Manager 
                 1. Check if a particular type of 
                 Type of message 
                 List of messages 
               
               
                 Adherence to 
                   
                 message (TBD by the customer) is 
                 (i.e. class 
                 (daily/weekly 
               
               
                 Message 
                   
                 being sent to the appropriate people as 
                 definition, e.g. 
                 reports), list of 
               
               
                 Addressing 
                   
                 per a predefined business process 
                 subject string 
                 non-compliant 
               
               
                 Rules 
                   
                 2. Track adherence to company 
                 identifier), 
                 users, aggregate 
               
               
                   
                   
                 policy 
                 recipient, 
                 volume 
               
               
                   
                   
                   
                 recipient 
                   
               
               
                   
                   
                   
                 addressing type 
                   
               
               
                   
                   
                   
                 (BCC, CC), 
                   
               
               
                   
                   
                   
                 sender, date range 
                   
               
               
                   
                   
                   
                 List of messages 
                   
               
               
                   
                   
                   
                 (daily/weekly 
                   
               
               
                   
                   
                   
                 reports), list of 
                   
               
               
                   
                   
                   
                 non-compliant 
                   
               
               
                   
                   
                   
                 users, aggregate 
                   
               
               
                   
                   
                   
                 volume 
                   
               
               
                 View 
                 Manager, 
                 1. View the distribution of 
                 Recipients, 
                 Charts for trend 
               
               
                 Customer, 
                 Executive 
                 messages for specified recipients and 
                 sender, date 
                 of messages 
               
               
                 Partner and 
                   
                 external domains over a given period. 
                 range, defined 
                 volume (all or 
               
               
                 Competitive 
                   
                 2. Understand my communications 
                 recipient groups 
                 top 10), 
               
               
                 Communications 
                   
                 with Customers, Partners, and 
                 and/or external 
                 messages from 
               
               
                 (Distribution &amp; 
                   
                 Competitors. For example, determine 
                 domains 
                 pre-defined 
               
               
                 Trends) 
                   
                 who is my business talking to and 
                   
                 group, group by 
               
               
                   
                   
                 why. 
                   
                 recipients or 
               
               
                   
                   
                 3. Understand the relationship with 
                   
                 domains, Pivot 
               
               
                   
                   
                 your customers, partners, and 
                   
                 Viewer for 
               
               
                   
                   
                 competitors. 
                   
                 exploring the 
               
               
                   
                   
                   
                   
                 data. 
               
               
                 Audit 
                 Manager, 
                 1. View message details of 
                 Recipients, 
                 Message List and 
               
               
                 Customer, 
                 Executive 
                 communication with a specific 
                 sender, date 
                 Details 
               
               
                 Partner or 
                   
                 partner, customer, or competitor 
                 range, defined 
                   
               
               
                 Competitive 
                   
                 2. Audit or understand my 
                 recipient groups 
                   
               
               
                 Communications 
                   
                 company&#39;s communication on a 
                 and/or external 
                   
               
               
                   
                   
                 particular partner, customer, or 
                 domains 
                   
               
               
                   
                   
                 competitor event or issue. 
                   
                   
               
               
                 Understand 
                 Management, 
                 1. Understand the distribution of 
                 Personal 
                 Charts for trend 
               
               
                 Personal 
                 Messaging 
                 messages going to and from personal 
                 messaging system 
                 of messages 
               
               
                 Messaging 
                 Administrator 
                 messaging systems such as Yahoo!, 
                 (as defined by the 
                 volume (all or 
               
               
                 System Use 
                   
                 Hotmail, and Gmail. 
                 user), recipients, 
                 top 10), 
               
               
                   
                   
                 2. Measure employee productivity 
                 sender, date 
                 messages from 
               
               
                   
                   
                 and gauge use and misuse of the 
                 range, defined 
                 pre-defined 
               
               
                   
                   
                 corporate messaging system. 
                 recipient groups 
                 group, group by 
               
               
                   
                   
                 3. Identify usage trends. 
                 and/or external 
                 recipients or 
               
               
                   
                   
                   
                 domains 
                 domains, Pivot 
               
               
                   
                   
                   
                   
                 Viewer to find 
               
               
                   
                   
                   
                   
                 out top personal 
               
               
                   
                   
                   
                   
                 messaging 
               
               
                   
                   
                   
                   
                 users/groups, etc. 
               
               
                 View Relayed 
                 Management 
                 1. As a messaging provider, 
                 Message type 
                 Charts for 
               
               
                 Traffic 
                   
                 understand volumes of re-routed 
                 (filter of messages 
                 trending 
               
               
                   
                   
                 messages. 
                 to include), Date 
                 aggregate 
               
               
                   
                   
                 2. Understand how my messaging 
                 range 
                 volume 
               
               
                   
                   
                 business is performing. 
                   
                   
               
               
                 Understand 
                 Manager 
                 1. View communication trends 
                 Target Users and 
                 Charts for 
               
               
                 Communication 
                   
                 between users and groups in my 
                 Groups, date 
                 trending of 
               
               
                 Patterns in 
                   
                 organization; includes multiple 
                 range, Message 
                 messages 
               
               
                 my 
                   
                 communication platforms.  
                 Type Filter 
                 volume, 
               
               
                 Organization 
                   
                 2. Compare the number of 
                   
                 Topological 
               
               
                   
                   
                 messages sent to a particular users, 
                   
                 Views, Pivot 
               
               
                   
                   
                 divided by TO:, CC:, BCC: 
                   
                 View 
               
               
                   
                   
                 3. Understand how my business is 
                   
                   
               
               
                   
                   
                 operating (e.g. what “silo groups” 
                   
                   
               
               
                   
                   
                 exist, which groups are talking to most 
                   
                   
               
               
                   
                   
                 to each other). 
                   
                   
               
               
                   
                   
                 4. Understand how my business is 
                   
                   
               
               
                   
                   
                 adhering to corporate directives. 
                   
                   
               
               
                 Understand the 
                 Management, 
                 1. Trend and see the use of 
                 Message Type 
                 Charts for trends 
               
               
                 Usage of 
                 IT Manager 
                 different types of messages in my 
                 (user defined), 
                 of different types 
               
               
                 Different 
                   
                 messaging system. 
                 Date range 
                 of messages, 
               
               
                 Types of 
                   
                 2. Determine the ratio of internal 
                   
                 Pivot Viewer 
               
               
                 Messages 
                   
                 vs. external communication. 
                   
                   
               
               
                   
                   
                 3. Get insight into specific business 
                   
                   
               
               
                   
                   
                 usage of my messaging system. 
                   
                   
               
               
                 Assess Mobile 
                 Management 
                 1. See what messages were stored 
                 Date range, 
                 List of message 
               
               
                 Data Leakage 
                   
                 or sent from a mobile device. 
                 inbound/outbound, 
                 and message 
               
               
                 Risk 
                   
                 2. Perform a mobile device data 
                 message type 
                 details. Charts 
               
               
                   
                   
                 leakage audit. 
                 (sender, recipient,  
                 for mobile 
               
               
                   
                   
                   
                 etc . . . , “mobile 
                 message usage 
               
               
                   
                   
                   
                 message” is 
                   
               
               
                   
                   
                   
                 inherent) 
                   
               
               
                 Track Implicit 
                 IT Manager, 
                 1. Track the percentage of 
                 Message subject, 
                 Distribution of 
               
               
                 Acknowledge- 
                 Management 
                 employees that have received and read  
                 sender 
                 message status 
               
               
                 ment of 
                   
                 an important message. 
                   
                 (received, read, 
               
               
                 Important 
                   
                 2. Report to HR or legal the 
                   
                 deleted without 
               
               
                 Message 
                   
                 progress and completion of the 
                   
                 being read), with 
               
               
                   
                   
                 distribution of the message. 
                   
                 the option of 
               
               
                   
                   
                   
                   
                 detailed list of 
               
               
                   
                   
                   
                   
                 status per people 
               
               
                 Track 
                 HR Manager, 
                 1. Track the distribution path of a  
                 Message subject, 
                 Full message 
               
               
                 Sensitive 
                 IT Manager, 
                 sensitive message. 
                 sender, date time 
                 delivery path 
               
               
                 Message 
                 Management 
                 2. Audit unauthorized distribution 
                 range, type 
                 (people &amp; 
               
               
                 Leakage 
                   
                 of sensitive information, 
                 (FWD, etc.) 
                 endpoint) of the 
               
               
                   
                   
                   
                   
                 message 
               
               
                   
                   
                   
                   
                 forwarding and 
               
               
                   
                   
                   
                   
                 delivery, and 
               
               
                   
                   
                   
                   
                 actions taken by 
               
               
                   
                   
                   
                   
                 users 
               
               
                 Analyze Usage 
                 Messaging 
                 1. Understand who, and how many 
                 Recipient(s), date 
                 Count/ratio of 
               
               
                 of Encrypted 
                 Administrator, 
                 encrypted messages are being sent 
                 range 
                 encrypted 
               
               
                 Message 
                 Management 
                 2. Ensure that the correct format is 
                   
                 messages, 
               
               
                   
                   
                 being used on my classified/non- 
                   
                 message-detail 
               
               
                   
                   
                 classified networks 
                   
                 on encrypted 
               
               
                   
                   
                   
                   
                 messages. 
               
               
                   
               
             
          
         
       
     
     
       
         
               
               
               
               
               
             
           
               
                 TABLE 5 
               
               
                   
               
               
                   
                 USER 
                   
                   
                   
               
               
                 USE CASE 
                 PERSONA 
                 POTENTIAL OBJECTIVE(S) 
                 INPUT 
                 OUTPUT 
               
               
                   
               
             
             
               
                 Track CAN- 
                 IT Manager, 
                 1. Alert or report whenever external 
                 Configure report 
                 Alert (email 
               
               
                 SPAM 
                 Management 
                 messages are sent with potentially 
                 (domain and other 
                 notification, 
               
               
                 Message 
                   
                 false header information (for example, 
                 routing 
                 dashboard). 
               
               
                 Header 
                   
                 From, To, Reply To or other routing 
                 specifications) 
                 Report (sender, 
               
               
                 Compliance 
                   
                 information that doesn&#39;t match 
                   
                 recipient, # of 
               
               
                   
                   
                 corporate domains or other server 
                   
                 recipients, 
               
               
                   
                   
                 configurations). 
                   
                 message 
               
               
                   
                   
                 2. Ensure that my company is 
                   
                 contents) 
               
               
                   
                   
                 adhering to CAN-SPAM requirements 
                   
                   
               
               
                 Track CAN- 
                 IT Manager, 
                 1. Alert or report whenever external 
                 Configure report 
                 Alert (email 
               
               
                 SPAM 
                 Management 
                 messages are sent without obligatory 
                 (enter “static” 
                 notification, 
               
               
                 Message 
                   
                 information (Physical postal address, 
                 search strings) 
                 dashboard), 
               
               
                 Content 
                   
                 disclosure that message is an ad, 
                   
                 Report (sender, 
               
               
                 Omissions 
                   
                 information on opting out of mailing 
                   
                 recipient, 
               
               
                   
                   
                 list). 
                   
                 message 
               
               
                   
                   
                 2. Ensure that my company is 
                   
                 contents, which 
               
               
                   
                   
                 adhering to CAN-SPAM 
                   
                 string(s) missing) 
               
               
                   
                   
                 requirements. 
                   
                   
               
               
                 Audit CAN- 
                 IT Manager, 
                 1. Ensure that a 3rd party 
                 Domains, routing 
                 Report (sender, 
               
               
                 SPAM 
                 Management 
                 contractor who&#39;s sending marketing 
                 info, required 
                 recipient, 
               
               
                 Compliance 
                   
                 messages on my (ensure verified 
                 strings 
                 message 
               
               
                 for 3rd Party 
                   
                 header information and required 
                   
                 contents, which 
               
               
                 Mailers 
                   
                 content strings). 
                   
                 string(s) missing) 
               
               
                   
                   
                 2. Ensure that my company is 
                   
                   
               
               
                   
                   
                 adhering to CAN-SPAM 
                   
                   
               
               
                   
                   
                 requirements. 
                   
                   
               
               
                 Monitor 
                 IT Manager, 
                 1. Alert or report whenever 
                 Configure report 
                 Alert (email 
               
               
                 Outgoing/Incoming  
                 Management 
                 outgoing or incoming messages are 
                 (specify likely 
                 notification, 
               
               
                 Messages 
                   
                 sent containing unauthorized personal 
                 string formats) 
                 dashboard), 
               
               
                 for Credit Card 
                   
                 data (such as CC numbers). 
                   
                 Report (sender, 
               
               
                 #s (PCI-DSS) 
                   
                 2. Ensure adherence to PCI-DSS 
                   
                 recipient, flagged 
               
               
                   
                   
                 requirements. 
                   
                 string, report/ 
               
               
                   
                   
                   
                   
                 allow) 
               
               
                 Monitor 
                 IT Manager, 
                 1. Alert or report whenever 
                 Configure report 
                 Alert (email 
               
               
                 Routing of 
                 Management 
                 outgoing or incoming messages are 
                 (specify identity 
                 notification, 
               
               
                 Sensitive 
                   
                 sent containing specific corporate 
                 strings) 
                 dashboard), 
               
               
                 Information 
                   
                 information not intended for 
                   
                 Report (sender, 
               
               
                   
                   
                 distribution (Financial disclosures, 
                   
                 recipient, flagged 
               
               
                   
                   
                 trade secrets, IPR). 
                   
                 string) 
               
               
                   
                   
                 2. Ensure adherence to the 
                   
                   
               
               
                   
                   
                 US APATRIOT requirements. 
                   
                   
               
               
                 Monitor 
                 IT Manager, 
                 1. Audit the messaging 
                 Report criteria, 
                 Executive/ 
               
               
                 Overall 
                 Management 
                 infrastructure for the purpose of 
                 specify network 
                 Detailed Report 
               
               
                 Messaging 
                   
                 general risk-management and 
                 components, 
                 for risk areas, 
               
               
                 Environment 
                   
                 mitigation against system health 
                 compliance 
                 overall risk 
               
               
                 to Identify 
                   
                 Identify failures, threats, intrusions, 
                 benchmarks 
                 benchmark, 
               
               
                 Potential 
                   
                 viruses, or other vulnerabilities that 
                   
                 export 
               
               
                 Vulnerabilities 
                   
                 may impact confidence in the integrity 
                   
                   
               
               
                   
                   
                 of the system. 
                   
                   
               
               
                   
                   
                 2. Perform regular assessments of 
                   
                   
               
               
                   
                   
                 risk will assist in meeting corporate 
                   
                   
               
               
                   
                   
                 commitments for Sarbanes-Oxley/ 
                   
                   
               
               
                   
                   
                 Gramm-Leach- Billey, Basel, etc. 
               
               
                   
               
             
          
         
       
     
     TERMINOLOGY 
     A number of computing systems have been described throughout this disclosure. The descriptions of these systems are not intended to limit the teachings or applicability of this disclosure. Further, the processing of the various components of the illustrated systems can be distributed across multiple machines, networks, and other computing resources. For example, the query manager  242  and the query security manager  244  can each be implemented as separate computing systems, or alternatively, as one server or computing system. In addition, two or more components of a system can be combined into fewer components. Further, various components of the illustrated systems can be implemented in one or more virtual machines, rather than in dedicated computer hardware systems. Likewise, the data repositories shown can represent physical and/or logical data storage, including, for example, storage area networks or other distributed storage systems. Moreover, in some embodiments the connections between the components shown represent possible paths of data flow, rather than actual connections between hardware. While some examples of possible connections are shown, any subset of the components shown can communicate with any other subset of components in various implementations. 
     Depending on the embodiment, certain acts, events, or functions of any of the algorithms described herein can be performed in a different sequence, can be added, merged, or left out altogether (e.g., not all described acts or events are necessary for the practice of the algorithms). Moreover, in certain embodiments, acts or events can be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors or processor cores or on other parallel architectures, rather than sequentially. Although certain computer-implemented tasks are described as being performed by a particular entity, other embodiments are possible in which these tasks are performed by a different entity. 
     Each of the various illustrated systems may be implemented as a computing system that is programmed or configured to perform the various functions described herein. The computing system may include multiple distinct computers or computing devices (e.g., physical servers, workstations, storage arrays, etc.) that communicate and interoperate over a network to perform the described functions. Each such computing device typically includes a processor (or multiple processors) that executes program instructions or modules stored in a memory or other non-transitory computer-readable storage medium. The various functions disclosed herein may be embodied in such program instructions, although some or all of the disclosed functions may alternatively be implemented in application-specific circuitry (e.g., ASICs or FPGAs) of the computer system. Where the computing system includes multiple computing devices, these devices may, but need not, be co-located. The results of the disclosed methods and tasks may be persistently stored by transforming physical storage devices, such as solid state memory chips and/or magnetic disks, into a different state. Each service described, such as those shown in  FIG. 3 , may be implemented by one or more computing devices, such as one or more physical servers programmed with associated server code. 
     Conditional language used herein, such as, among others, “can,” “might,” “may,” “e.g.,” and the like, unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements and/or states. Thus, such conditional language is not generally intended to imply that features, elements and/or states are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without author input or prompting, whether these features, elements and/or states are included or are to be performed in any particular embodiment. 
     While the above detailed description has shown, described, and pointed out novel features as applied to various embodiments, it will be understood that various omissions, substitutions, and changes in the form and details of the devices or algorithms illustrated can be made without departing from the spirit of the disclosure. As will be recognized, the processes described herein can be embodied within a form that does not provide all of the features and benefits set forth herein, as some features can be used or practiced separately from others. The scope of protection is defined by the appended claims rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope. 
     For purposes of this disclosure, an information handling system may include any instrumentality or aggregate of instrumentalities operable to compute, calculate, determine, classify, process, transmit, receive, retrieve, originate, switch, store, display, communicate, manifest, detect, record, reproduce, handle, or utilize any form of information, intelligence, or data for business, scientific, control, or other purposes. For example, an information handling system may be a personal computer (e.g., desktop or laptop), tablet computer, mobile device (e.g., personal digital assistant (PDA) or smart phone), server (e.g., blade server or rack server), a network storage device, or any other suitable device and may vary in size, shape, performance, functionality, and price. The information handling system may include random access memory (RAM), one or more processing resources such as a central processing unit (CPU) or hardware or software control logic, ROM, and/or other types of nonvolatile memory. Additional components of the information handling system may include one or more disk drives, one or more network ports for communicating with external devices as well as various input and output (I/O) devices, such as a keyboard, a mouse, touchscreen and/or a video display. The information handling system may also include one or more buses operable to transmit communications between the various hardware components. 
     Although various embodiments of the method and apparatus of the present invention have been illustrated in the accompanying Drawings and described in the foregoing Detailed Description, it will be understood that the invention is not limited to the embodiments disclosed, but is capable of numerous rearrangements, modifications and substitutions without departing from the spirit of the invention as set forth herein.