Patent Publication Number: US-8996684-B2

Title: Scoring and interpreting change data through inference by correlating with change catalogs

Description:
TECHNICAL FIELD 
     Embodiments relate to the field of compliance assessment, in particular to methods and apparatuses for scoring and interpreting change data through inference by correlating with change catalogs in order to facilitate categorization of system change information. 
     BACKGROUND 
     Generally, most organizations that utilize computer systems and networks monitor those systems and networks for changes. This monitoring is often initiated and performed by a server that requests a number of client settings from monitored computing devices, servers, peripheral devices, etc. within the system or network. Upon receiving the settings, the server may then analyze, classify, and/or store them, and issue a report that indicates changes to the settings. Often, such a report includes numerous occurrences of various changes. Indeed, the number of occurrences may be so numerous that it renders the report nearly useless. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments of the disclosure will be described by way of exemplary embodiments, but not limitations, illustrated in the accompanying drawings in which like references denote similar elements, and in which: 
         FIG. 1  illustrates a system-level view of various embodiments of the disclosure; 
         FIG. 2  illustrates an operational overview of a change collection and analysis, in accordance with various embodiments; 
         FIG. 3  illustrates a flow chart view of selected operations of the methods of various embodiments; and 
         FIG. 4  illustrates an example computer system suitable for use to practice aspects of various embodiments. 
     
    
    
     DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS 
     Illustrative embodiments include, but are not limited to, methods, systems, and articles for receiving, by a monitor server, change data associated with a change captured on a target host. In various embodiments, the target host may have provided the change data in response to detecting the change, and the change data may include one or more rules, settings, and/or parameters. Further, in some embodiments, the target host may detect a change to a rule, setting, or parameter based on a collection policy defining what change data is to be collected by the target host and provide data associated with the rule, setting, or parameter as change data to the monitor server. 
     Various aspects of the illustrative embodiments will be described using terms commonly employed by those skilled in the art to convey the substance of their work to others skilled in the art. However, it will be apparent to those skilled in the art that alternate embodiments may be practiced with only some of the described aspects. For purposes of explanation, specific numbers, materials, and configurations are set forth in order to provide a thorough understanding of the illustrative embodiments. However, it will be apparent to one skilled in the art that alternate embodiments may be practiced without the specific details. In other instances, well-known features are omitted or simplified in order not to obscure the illustrative embodiments. 
     Further, various operations will be described as multiple discrete operations, in turn, in a manner that is most helpful in understanding the illustrative embodiments; however, the order of description should not be construed as to imply that these operations are necessarily order dependent. In particular, these operations need not be performed in the order of presentation. 
     The phrase “in one embodiment” is used repeatedly. The phrase generally does not refer to the same embodiment; however, it may. The terms “comprising,” “having,” and “including” are synonymous, unless the context dictates otherwise. The phrase “A/B” means “A or B”. The phrase “A and/or B” means “(A), (B), or (A and B)”. The phrase “at least one of A, B and C” means “(A), (B), (C), (A and B), (A and C), (B and C) or (A, B and C)”. The phrase “(A) B” means “(B) or (A B)”, that is, A is optional. 
       FIG. 1  illustrates a system-level view of various embodiments of the disclosure. As illustrated, a target host  102  may be communicatively coupled to a monitor server  106 . The monitor server  106  may monitor the target host  102  for changes and may determine whether rules, settings, and/or configuration parameters of the target host  102  have changed and, in various embodiments, may determine whether the rules, settings, and/or configuration parameters of the target host meet one or more compliance policies  110 . 
     In various embodiments, target host  102  may include one or more rules or collection policies  104  for use in capturing changes to data of the target host  102 , such as changes to rules, settings, and/or configuration parameters. Upon detecting/capturing a change, the target host  102  may provide data associated with the change to the monitor server  106  to store in a change database  108  of the monitor server  106 . The monitor server  106  may then look up all compliance policies  110  that match collection policies or rules  104  specified in the received change data. In some embodiments, the monitor server  106  may filter the change data and determine whether one or more rules, settings, and/or parameters of the change data meet one or more compliance policies  110 . The determining may include evaluating an expression of at least one of the compliance policies  110  against element data specified in the change data. In various embodiments, the monitor server  106  may then generate test results based on whether the compliance policies  110  were met. In one embodiment, the monitor server  106  may further generate a report. The report may then be provided to target host  102 , and/or an administrative user of monitor server  106 , or to some other system. 
     In various embodiments, target host  102  and monitor server  106  may be any sort of computing devices known in the art. The computing devices may be personal computers (PC), workstations, servers, routers, mainframes, modular computers within blade servers or high-density servers, personal digital assistants (PDA), entertainment centers, set-top boxes, or mobile devices. An exemplary computing device is illustrated by  FIG. 4 , and will be described in greater detail herein. Target host  102  may be some type of peripheral device or service device, such as, for example, a printer, a copier, a fax machine, etc. 
     In some embodiments, monitor server  106  and target host  102  may be deployed on computing devices of the same organization. In other embodiments, monitor server  106  may belong to a separate organization, such as a compliance monitoring organization whose purpose is to monitor and ensure industry standards. Also, in one embodiment, target host  102  and monitor server  106  may be separate logical components or virtual machines of the same computing device. 
     In various embodiments, as mentioned above, target host  102  may have one or more collection policies or rules  104 , and monitor server  106  may have a change database  108  and one or more compliance policies  110 . These components and associated data and logic are also illustrated in  FIG. 2  and will be described herein in greater detail. 
     In various embodiments, where target host  102  and monitor server  106  are remotely disposed from each other, they may be communicatively coupled to each other. In some embodiments, the computing devices may be coupled by a networking fabric (not illustrated). Such a networking fabric may include one or more of a local area network (LAN), a wide area network (WAN), and the Internet, as is known in the art. In one embodiment, the networking fabric may comprise a private network or a virtual private network (VPN) that may utilize tunneling. In some embodiments, where target host  102  and monitor server  106  belong to the same organization, they may be coupled by one or more private LANs or WANs of the organization. 
       FIG. 2  illustrates an operational overview of change collection and analysis, in accordance with various embodiments. In various embodiments, collecting change data  202  may be accomplished by logic of the target host collecting all changes that are captured/detected on the target host, or may be accomplished by logic of the target host applying collection policies of rules to changes that are captured/detected on the target host. In various embodiments, the captured/detected change may be associated with other descriptive data to form change data  202 . For example, the change data  202  for a given change may include an identification of the node or target host  102  on which the change was captured, the rule or collection policy  104  responsible for the capturing of the change (if applied), a name of the data element (such as a rule, setting, or configuration parameter) for which the change was detected, and the element data of the element for which the change was detected. Change data  202  may also simply be a listing of changes. 
     In some embodiments, the collection policies  104  and the logic for applying them may be used to monitor a remote host. In such embodiments, the collection policies  104  and logic may be located on monitor server  106 , or another device, and may be used to remotely detect changes on target host  102 . Changes may be detected and collected automatically or manually. Changes may be detected and collected periodically or when simply desired. 
     In various embodiments, upon being generated, change data  202  may be stored in change database  108 . As mentioned above, in some embodiments change database  108  may be a database of the monitor server  106 . In other embodiments, change database  108  may reside on a different computing device then monitor server  106 . For example, change database  108  may reside on a database server device that is communicatively coupled to monitor server  106 . Further, in various embodiments, change database  108  may be any sort of database known in the art, such as a relational database, a normalized or de-normalized database, a data structure, or an unformatted file. In some embodiments, change database  108  may store all change data  202  received from target hosts. In some embodiments, change database  108  may have a data retention policy and may discard change data  202  after a specified/pre-determined duration of time. 
     In some embodiments, the monitor server  106  then generates a change data report based on the collected change data and may store the report or provide it to the target host  102 , an administrative user through a user interface of monitor server  106 , and/or some other system. 
     The change data report generally includes numerous instances of changes within change data  202 . Indeed, the number of instances of changes may number in the thousands. Thus, a user analyzing the change data report may have difficulty ascertaining how or what caused all of the changes. However, in many circumstances, many of the changes are based upon a common known change that has occurred within the target host, or over the entire enterprise or system being monitored. For example, if a new software application has been loaded onto the target host, then, as known to those skilled in the art, there will be numerous changes throughout the target host. 
     Thus, in accordance with the various embodiments of the present invention, the monitor server  106 , upon receiving the change data associated with a plurality of changes captured on the target host  102  may analyze the change data in order to group  208  the instances of changes into clusters of changes that may be related, i.e., may be related as to a cause or reason for the changes. Various statistical techniques and inference techniques may be used to cluster the instances of changes into the various clusters. Such techniques are generally well-known to those skilled in the art and are generally based upon known occurrences or events. For example, given the known installation of a new software application, the monitor server  106  may analyze and classify the instances of changes based upon anticipated changes that would be expected to occur given the installation of the new software application. 
     In accordance with various embodiments, one or more change catalogs may be included within the monitor server database. Alternatively, the one or more change catalogs may be located separately from the monitor server database, and even the monitor server  106  entirely. Such change catalogs may include information relating to reasons, events, etc., that may indicate why certain change(s) have occurred. The change catalogs may include, for example, expected changes, expected rule violations, expected parameter changes and expected setting changes that may occur due to various events, occurrences, etc. For example, for a new software application that may be loaded onto the target host, then a change catalog may include expected changes, expected rule violations, expected parameter changes and expected setting changes that may occur due to the loading of the software application onto the target host. Accordingly, correlating  210  the change data with the change catalog may facilitate clustering and/or categorization of the instances of changes that may be due to the loading of the new software application onto the target host. Thus, using various inference techniques, the change data may be analyzed, clustered and categorized based upon correlation  210  with the one or more change catalogs. Such a comparison may then allow the various instances of changes to be categorized and clustered based upon correlation of the change data with the information within the change catalogs. 
     Once the change data have been classified as clusters, a classification report may be generated  212  providing classification or categorization and cluster information for the various changes. A user viewing such a classification report may now only have a few clusters or categories of changes to analyze as opposed to the potential thousands of individual changes. The report may indicate that at least one of the categories or clusters of changes relates, for example, to the installation of the new software application. The user may then determine whether the changes are acceptable or whether certain adjustments to the changes need to be made to bring the changes, settings, parameters, rules, etc., into compliance with the compliance policy. 
     In accordance with various embodiments of the present embodiments, the monitor server  106  may apply a compliance policy  110  to change data  202  to determine whether the one or more rules, settings, and/or configuration parameters specified in the change data  202  meet one or more compliance policies  110 . The determining may comprise evaluating  214  an expression specified in a compliance policy  110  against element data specified in the change data  202 . 
     In various embodiments, the monitor server  106  may generate  216  one or more test results based on the determining/evaluating. The test result may indicate either that the rule, setting, or configuration parameter specified in change data  202  is in compliance or not in compliance with compliance policy  110 . In various embodiments, the test results may then be stored in a test results database (not illustrated). In one embodiment, the test results database may be identical to the change database. 
     In some embodiments, the monitor server  106  may generate a test results report based on the test results and may store the report or provide it to the target host  102 , an administrative user through a user interface of monitor server  106 , and/or some other system. The test results report may include an indication of whether or not a given rule, setting, or parameter is in compliance and, if not in compliance, an indication of what an appropriate value or values for a compliant rule, setting, or parameter would be. In one embodiment, the monitor server  106  may provide the report to an industry standards/compliance monitoring organization. 
     The test results report generally includes numerous test results and may number in the thousands. Thus, a user analyzing the test results report may have difficulty ascertaining how or what caused all of the changes that resulted in the test results. However, in many circumstances, many of the test results are based upon a common known change that has occurred within the target host, or over the entire enterprise or system being monitored. For example, if a new software application has been loaded onto the target host, then, as known to those skilled in the art, there will be numerous changes throughout the target host. 
     Thus, in accordance with the various embodiments of the present invention, the monitor server  106 , upon receiving the test results associated with a plurality of changes captured on the target host  102  may analyze the test results in order to group  218  the test results into clusters of test results that may be related, i.e., may be related as to a cause or reason for the changes that led to the test results. Various statistical techniques and inference techniques may be used to cluster the test results into the various clusters. Such techniques are generally well-known to those skilled in the art and are generally based upon known occurrences or events. For example, given the known installation of a new software application, the monitor server  106  may analyze and classify the test results based upon anticipated changes that would be expected to occur given the installation of the new software application. 
     As previously noted, in accordance with various embodiments, one or more change catalogs may be included within the monitor server database. Alternatively, the one or more change catalogs may be located separately from the monitor server database, and even the monitor server  106  entirely. Such change catalogs may include information relating to reasons, events, etc., that may indicate why certain change(s) have occurred. The change catalogs may include, for example, expected changes, expected rule violations, expected parameter changes and expected setting changes that may occur due to various events, occurrences, etc. For example, for a new software application that may be loaded onto the target host, then a change catalog may include expected changes, expected rule violations, expected parameter changes and expected setting changes that may occur due to the loading of the software application onto the target host. Accordingly, correlating  220  the test results with the change catalog may facilitate clustering and/or categorization of the test results that may be due to the loading of the new software application onto the target host. Thus, using various inference techniques, the test results may be analyzed, clustered and categorized based upon correlation  220  with the one or more change catalogs. Such a comparison may then allow the various test results to be categorized and clustered based upon correlation of the change data with the information within the change catalogs. 
     Once the test results have been classified as clusters, a classification report may be generated  222  providing classification or categorization and cluster information for the various changes that resulted in the test results. A user viewing such a classification report may now only have a few clusters or categories of test results/changes to analyze as opposed to the potential thousands of individual test results/changes. The report may indicate that at least one of the categories or clusters of test results/changes relates, for example, to the installation of the new software application. The user may then determine whether the changes are acceptable or whether certain adjustments to the changes need to be made to bring the changes, settings, parameters, rules, etc., into compliance with the compliance policy. 
       FIG. 3  illustrates a flow chart view of selected operations of the methods of various embodiments. As illustrated, a monitor server may receive change data captured on a target host, block  304 , the target host providing the change data in response to detecting changes, and the change data including one or more rules, settings, and/or parameters. 
     As is further illustrated, the monitor server may store the received change data in a change database, block  306 . The monitor server may group the change data into clusters, block  308 , and correlate the clusters with one or more change catalogs in order to group the change data into clusters, block  310 . Once the change data has been classified as clusters, a report may be generated providing classification and/or categorization and cluster information for the various changes, block  312 . In such embodiments, the generating may comprise generating a report to the target host and/or to an administrative user. 
       FIG. 4  illustrates an exemplary computer system suitable for use to practice aspects of various embodiments. As may be seen, computing system  400  includes a number of processors or processor cores  402 , and system memory  404 . For the purpose of this application, including the claims, the terms “processor” and “processor cores” may be considered synonymous, unless the context clearly requires otherwise. Additionally, computing system  400  includes mass storage devices  406  (such as diskette, hard drive, compact disc read only memory (CDROM), a disc storage device, and so forth), input/output devices  408  (such as display, keyboard, cursor control and so forth) and communication interfaces  410  (such as network interface cards, modems and so forth). The elements are coupled to each other via system bus  412 , which represents one or more buses. In the case of multiple buses, they are bridged by one or more bus bridges (not illustrated). 
     Each of these elements performs its conventional functions known in the art. In particular, system memory  404  and mass storage  406  may be employed to store a working copy and a permanent copy of the programming instructions implementing one or more aspects of the above described teachings to practice the various embodiments, herein collectively denoted as  422 . The various components may be implemented by assembler instructions supported by processor(s)  402  or high-level languages, such as, for example, C, that may be compiled into such instructions. 
     The permanent copy of the programming instructions may be placed into permanent storage  406  in the factory, or in the field, through, for example, a distribution medium (not illustrated), such as a compact disc (CD), or through communication interface  410  (from a distribution server (not illustrated)). That is, one or more distribution media having an implementation of the agent program may be employed to distribute the agent and program various computing devices. 
     The constitution of these elements  402 - 412  are generally known to one skilled in the art, and accordingly will not be further described. 
     In embodiments of the present invention, an article of manufacture (not illustrated) may be employed to implement one or more methods as disclosed herein. For example, in exemplary embodiments, an article of manufacture may comprise a storage medium and a plurality of programming instructions stored on the storage medium and configured to program a target host to detect a change to a rule, setting, or parameter based on a collection policy defining what change data is to be collected by the target host, and provide data associated with the rule, setting, or parameter as change data to a monitor server. The monitor server may determine whether the rule, setting, or parameter meet one or more compliance policies. In other exemplary embodiments, the plurality of programming instructions may be configured to program a monitor server to receive data associated with a change captured on a target host, the target host providing the data in response to detecting the change, and the data including one or more rules, settings, and/or parameters. The instructions may further be configured to correlate the change data with one or more change catalogs in order to group the change data into clusters. The instructions may also further be configured to generate a report providing classification and/or categorization and cluster information for the various changes. 
     Although specific embodiments have been illustrated and described herein, it will be appreciated by those of ordinary skill in the art that a wide variety of alternate and/or equivalent implementations may be substituted for the specific embodiments illustrated and described, without departing from the scope of the embodiments. This application is intended to cover any adaptations or variations of the embodiments discussed herein. Therefore, it is manifestly intended that the embodiments be limited only by the claims and the equivalents thereof.