# Patent

## Title
Computer implemented methods and apparatus for identifying similar labels using collaborative filtering

## Abstract

Disclosed are methods, apparatus, systems, and computer-readable storage media for identifying similar labels. In some implementations, one or more servers maintain a plurality of data entries in one or more database tables storing textual data, each data entry of a first portion of the data entries including: a text sequence, a label, and a text-to-label association score, and each data entry of a second portion of the data entries including: a first label, a second label, and a similarity score. The one or more servers analyze the data of the first portion of data entries to generate one or more pairs, each pair including information identifying a first label and a second label. The one or more servers calculate a similarity score for each of the one or more pairs and store the respective similarity scores in the second portion of the data entries.

## Background
PRIORITY DATA
This application claims priority to and commonly assigned U.S. Provisional Patent Application No. 61/755,056, filed on Jan. 22, 2013, entitled GROUPING SIMILAR TOPICS USING COLLABORATIVE FILTERING by Palmert et al., which is hereby incorporated by reference in its entirety and for all purposes.


COPYRIGHT NOTICE
A portion of the disclosure of this patent document contains material, which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever.
TECHNICAL FIELD
This patent document relates generally to providing services in an on-demand service environment using a database system and, more specifically, to computer-implemented techniques for assisting users of the on-demand service environment in performing tasks in the environment.
BACKGROUND
Organizations typically employ many different types of software and computing technologies to meet their computing needs. However, installing and maintaining software on an organization's own computer systems may involve one or more drawbacks. For example, when software must be installed on computer systems within the organization, the installation process often requires significant time commitments, since organization personnel may need to separately access each computer. Once installed, the maintenance of such software typically requires significant additional resources. Each installation of the software may need to be separately monitored, upgraded, and/or maintained. Further, organization personnel may need to protect each installed piece of software against viruses and other malevolent code. Given the difficulties in updating and maintaining software installed on many different computer systems, it is common for software to become outdated. Also, the organization will likely need to ensure that the various software programs installed on each computer system are compatible. Compatibility problems are compounded by frequent upgrading, which may result in different versions of the same software being used at different computer systems in the same organization.
Accordingly, organizations increasingly prefer to use on-demand services accessible via the Internet rather than software installed on in-house computer systems. On-demand services, often termed “cloud computing” services, take advantage of increased network speeds and decreased network latency to provide shared resources, software, and information to computers and other devices upon request. Cloud computing typically involves over-the-Internet provision of dynamically scalable and often virtualized resources. Technological details can be abstracted from the users, who no longer have need for expertise in, or control over, the technology infrastructure “in the cloud” that supports them.



BRIEF DESCRIPTION OF THE DRAWINGS
The included drawings are for illustrative purposes and serve only to provide examples of possible structures and operations for the disclosed inventive systems, apparatus, and methods for identifying similar labels using collaborative filtering. These drawings in no way limit any changes in form and detail that may be made by one skilled in the art without departing from the spirit and scope of the disclosed implementations.
FIG. 1A shows a block diagram of an example of an environment 10 in which an on-demand database service can be used in accordance with some implementations.
FIG. 1B shows a block diagram of an example of some implementations of elements of FIG. 1A and various possible interconnections between these elements.
FIG. 2A shows a system diagram illustrating an example of architectural components of an on-demand database service environment 200 according to some implementations.
FIG. 2B shows a system diagram further illustrating an example of architectural components of an on-demand database service environment according to some implementations.
FIG. 3 shows a flowchart of an example of a method 300 for tracking updates to a record stored in a database system, performed in accordance with some implementations.
FIG. 4 shows a block diagram of an example of components of a database system configuration 400 performing a method for tracking an update to a record according to some implementations.
FIG. 5 shows a flowchart of an example of a method 500 for tracking actions of a user of a database system, performed in accordance with some implementations.
FIG. 6 shows a flowchart of an example of a method 600 for creating a news feed from messages created by a user about a record or another user, performed in accordance with some implementations.
FIG. 7 shows a flowchart of an example of a computer implemented method 700 for identifying similar labels to a user, performed in accordance with some implementations.
FIG. 8 shows a flowchart of an example of a computer implemented method 800 for identifying similar labels to a user, performed in accordance with some implementations.
FIG. 9 shows a flowchart of an example of a computer implemented method 900 for identifying similar labels to a user, performed in accordance with some implementations.
FIG. 10A shows an example of a database table 1000 identifying text-to-label association scores for a text sequence and a label, according to some implementations.
FIG. 10B shows an example of a database table 1050 identifying similarity scores for a first label and a second label, according to some implementations.



DETAILED DESCRIPTION
Examples of systems, apparatus, methods and computer-readable storage media according to the disclosed implementations are described in this section. These examples are being provided solely to add context and aid in the understanding of the disclosed implementations. It will thus be apparent to one skilled in the art that implementations may be practiced without some or all of these specific details. In other instances, certain process/method operations also referred to herein as “blocks,” have not been described in detail in order to avoid unnecessarily obscuring implementations. Other applications are possible, such that the following examples should not be taken as definitive or limiting either in scope or setting.
In the following detailed description, references are made to the accompanying drawings, which form a part of the description and in which are shown, by way of illustration, specific implementations. Although these implementations are described in sufficient detail to enable one skilled in the art to practice the disclosed implementations, it is understood that these examples are not limiting, such that other implementations may be used and changes may be made without departing from their spirit and scope. For example, the blocks of methods shown and described herein are not necessarily performed in the order indicated. It should also be understood that the methods may include more or fewer blocks than are indicated. In some implementations, blocks described herein as separate blocks may be combined. Conversely, what may be described herein as a single block may be implemented in multiple blocks.
Various implementations described or referenced herein are directed to different methods, apparatus, systems, and computer-readable storage media for identifying similar labels to a user of an on-demand service environment, such as, for example, an online social network. Online social networks are increasingly becoming a common way to facilitate communication among people, any of whom can be recognized as users of a social networking system. One example of an online social network is Chatter®, provided by salesforce.com, inc. of San Francisco, Calif. salesforce.com, inc. is a provider of social networking services, customer relationship management (CRM) services and other database management services, any of which can be accessed and used in conjunction with the techniques disclosed herein in some implementations. These various services can be provided in a cloud computing environment, for example, in the context of a multi-tenant database system. Thus, the disclosed techniques can be implemented without having to install software locally, that is, on computing devices of users interacting with services available through the cloud. While the disclosed implementations are often described with reference to Chatter®, those skilled in the art should understand that the disclosed techniques are neither limited to Chatter® nor to any other services and systems provided by salesforce.com, inc. and can be implemented in the context of various other database systems and/or social networking systems such as Facebook®, LinkedIn®, Twitter®, Google+®, Yammer® and Jive® by way of example only.
Some online social networks can be implemented in various settings, including organizations. For instance, an online social network can be implemented to connect users within an enterprise such as a company or business partnership, or a group of users within such an organization. For instance, Chatter® can be used by employee users in a division of a business organization to share data, communicate, and collaborate with each other for various social purposes often involving the business of the organization. In the example of a multi-tenant database system, each organization or group within the organization can be a respective tenant of the system, as described in greater detail below.
In some online social networks, users can access one or more information feeds, which include information updates presented as items or entries in the feed. Such a feed item can include a single information update or a collection of individual information updates. A feed item can include various types of data including character-based data, audio data, image data and/or video data. An information feed can be displayed in a graphical user interface (GUI) on a display device such as the display of a computing device as described below. The information updates can include various social network data from various sources and can be stored in an on-demand database service environment. In some implementations, the disclosed methods, apparatus, systems, and computer-readable storage media may be configured or designed for use in a multi-tenant database environment.
In some implementations, an online social network may allow a user to follow data objects in the form of records such as cases, accounts, or opportunities, in addition to following individual users and groups of users. The “following” of a record stored in a database, as described in greater detail below, allows a user to track the progress of that record. Updates to the record, also referred to herein as changes to the record, are one type of information update that can occur and be noted on an information feed such as a record feed or a news feed of a user subscribed to the record. Examples of record updates include field changes in the record, updates to the status of a record, as well as the creation of the record itself. Some records are publicly accessible, such that any user can follow the record, while other records are private, for which appropriate security clearance/permissions are a prerequisite to a user following the record.
Information updates can include various types of updates, which may or may not be linked with a particular record. For example, information updates can be social media messages or can otherwise be generated in response to user actions or in response to events. Examples of social media messages include: posts, comments, indications of a user's personal preferences such as “likes” and “dislikes”, updates to a user's status, uploaded files, and user-submitted hyperlinks to social network data or other network data such as various documents and/or web pages on the Internet. Posts can include alpha-numeric or other character-based user inputs such as words, phrases, statements, questions, emotional expressions, and/or symbols. Comments generally refer to responses to posts or to other information updates, such as words, phrases, statements, answers, questions, and reactionary emotional expressions and/or symbols. Multimedia data can be included in, linked with, or attached to a post or comment. For example, a post can include textual statements in combination with a JPEG image or animated image. A like or dislike can be submitted in response to a particular post or comment. Examples of uploaded files include presentations, documents, multimedia files, and the like.
Various implementations described or referenced herein are directed to different methods, apparatus, systems, and computer-readable storage media for identifying similar labels using collaborative filtering. For instance, users of an on-demand service environment may be creating social media messages or articles, and they may be assigning labels to the objects. An example of a label is a user-generated topic. Because users are often permitted to create and customize their own topics, there may be many similar topics being applied to various data objects. Similar labels may be labels that have different spellings, but are variations of the same word or phrase. As an example, “politics” and “political” may be similar labels for posts that users create that are deemed political in nature. In a database system, however, the “politics” and “political” labels exist as different entities. Some of the disclosed implementations are configured to detect similar labels such as “politics” and “political” and to identify the similar labels to be merged, de-duplicated or otherwise processed.
Similar labels, rather than being variations of the same word or phrase, may also be synonymous or otherwise applied to social media messages having similar content with regard to subject matter. As an example, “Republican” and “GOP” may be similar labels for posts that users create that pertain to the Republican Party in general. In some implementations, the similar labels, rather than being single words, may be phrases or even sentences that are applied to a social media message.
Some of the disclosed implementations relate to a server configured to identify similar labels based on the contents of the social media messages and other textual objects to which the labels are applied. As an example, where the labels are user-generated topics, by maintaining a database of words of messages to which a topic has been applied, a server may keep track of how often any particular word is part of a message to which a particular topic has been applied. Using this data, collaborative filtering methods may be used to identify similar topics based on the similarity of the words that appear in messages to which the topics have been applied.
Collaborative filtering (CF) generally refers to a process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, and so forth. For example, in such a model, it can be assumed that where user A performs task 3 after performing tasks 1 and 2, user B, who has performed tasks 1 and 2, is more likely to perform task 3 than if user A had not performed task 3 after performing tasks 1 and 2. In some disclosed implementations, collaborative filtering methods may be applied to a first vector for a first label and a second vector for a second label, wherein the first vector for the first label includes all of the words that have appeared in messages to which the first label has been applied, and wherein the second vector for the second label includes all of the words that have appeared in messages to which the second label has applied. The CF method may generate a cosine similarity score indicating the similarity between the first set of words for the first label and the second set of words for the second label.
A threshold similarity level may be set for each label for which similar labels are being identified. As an example, if the server designates a label “politics” and a threshold similarity level of 80%, then the CF methods may be performed on the pairwise label combinations involving the “politics” label, and all labels for which the CF similarity score exceeds 80% may be identified as being similar to the label “politics” and suggested to a user to be merged with the “politics” label. Using these groupings, similar labels, such as user-generated topics that have the same or very similar meanings, can be de-duplicated.
Some of the disclosed implementations may be applied to identifying similar objects with which text is associated and to which a label may be applied. For example, a user may be associated with all of the social media posts that he generates, and most of his posts may have to do with the topic of sports. The user may be given, by himself or by another user, a label, such as “sports writer.” Another user may also write a number of social media posts on the topic of sports, and the label given him may be “sports columnist.” Based on the contents of what users labeled “sports writer” and users labeled “sports columnist” generate, a server using collaborative filtering methods may suggest that the labels “sports writer” and “sports columnist” are similar and should be merged.
As another example, the disclosed implementations may use collaborative filtering methods to identify similar text documents to which different file names have been applied. The text documents may be similar because they are different versions of the same original documents. Some of the versions may have the same file name, but slightly different contents. Some of the versions may have a different file name and slightly different contents. Because they have different file names, it may be difficult to determine that these different text documents are actually different versions of the same original document. Collaborative filtering methods may use the contents of all of the text documents to which different file names have been applied to determine which files are similar and relate to the same original document.
In some of the disclosed implementations, the recommended group of similar labels may be presented in various ways. The server may periodically identify groups of similar topics, and the similar topics may be transmitted to a computing device to be displayed as pairs of similar topics or to be displayed by topic, in which all topics that are found to be similar to one topic are grouped together.
These and other implementations may be embodied in various types of hardware, software, firmware, and combinations thereof. For example, some techniques disclosed herein may be implemented, at least in part, by computer-readable media that include program instructions, state information, etc., for performing various services and operations described herein. Examples of program instructions include both machine code, such as produced by a compiler, and files containing higher-level code that may be executed by a computing device such as a server or other data processing apparatus using an interpreter. Examples of computer-readable media include, but are not limited to, magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM disks; magneto-optical media; and hardware devices that are specially configured to store program instructions, such as read-only memory (“ROM”) devices and random access memory (“RAM”) devices. These and other features of the disclosed implementations will be described in more detail below with reference to the associated drawings.
The term “multi-tenant database system” can refer to those systems in which various elements of hardware and software of a database system may be shared by one or more customers. For example, a given application server may simultaneously process requests for a great number of customers, and a given database table may store rows of data such as feed items for a potentially much greater number of customers. The term “query plan” generally refers to one or more operations used to access information in a database system.
A “user profile” or “user's profile” is generally configured to store and maintain data about a given user of the database system. The data can include general information, such as name, title, phone number, a photo, a biographical summary, and a status, e.g., text describing what the user is currently doing. As mentioned below, the data can include messages created by other users. Where there are multiple tenants, a user is typically associated with a particular tenant. For example, a user could be a salesperson of a company, which is a tenant of the database system that provides a database service.
The term “record” generally refers to a data entity, such as an instance of a data object created by a user of the database service, for example, about a particular (actual or potential) business relationship or project. The data object can have a data structure defined by the database service (a standard object) or defined by a user (custom object). For example, a record can be for a business partner or potential business partner (e.g., a client, vendor, distributor, etc.) of the user, and can include information describing an entire company, subsidiaries, or contacts at the company. As another example, a record can be a project that the user is working on, such as an opportunity (e.g., a possible sale) with an existing partner, or a project that the user is trying to get. In one implementation of a multi-tenant database system, each record for the tenants has a unique identifier stored in a common table. A record has data fields that are defined by the structure of the object (e.g., fields of certain data types and purposes). A record can also have custom fields defined by a user. A field can be another record or include links thereto, thereby providing a parent-child relationship between the records.
The terms “information feed” and “feed” are used interchangeably herein and generally refer to a combination (e.g., a list) of feed items or entries with various types of information and data. Such feed items can be stored and maintained in one or more database tables, e.g., as rows in the table(s), that can be accessed to retrieve relevant information to be presented as part of a displayed feed. The term “feed item” (or feed element) refers to an item of information, which can be presented in the feed such as a post submitted by a user. Feed items of information about a user can be presented in a user's profile feed of the database, while feed items of information about a record can be presented in a record feed in the database, by way of example. A profile feed and a record feed are examples of different information feeds. A second user following a first user and a record can receive the feed items associated with the first user and the record for display in the second user's news feed, which is another type of information feed. In some implementations, the feed items from any number of followed users and records can be combined into a single information feed of a particular user.
As examples, a feed item can be a social media message, such as a user-generated post of text data, and a feed tracked update to a record or profile, such as a change to a field of the record. Feed tracked updates are described in greater detail below. A feed can be a combination of messages and feed tracked updates. Messages include text created by a user, and may include other data as well. Examples of messages include posts, user status updates, and comments. Messages can be created for a user's profile or for a record. Posts can be created by various users, potentially any user, although some restrictions can be applied. As an example, posts can be made to a wall section of a user's profile page (which can include a number of recent posts) or a section of a record that includes multiple posts. The posts can be organized in chronological order when displayed in a graphical user interface (GUI), for instance, on the user's profile page, as part of the user's profile feed. In contrast to a post, a user status update changes a status of a user and can be made by that user or an administrator. A record can also have a status, the update of which can be provided by an owner of the record or other users having suitable write access permissions to the record. The owner can be a single user, multiple users, or a group. In one implementation, there is only one status for a record.
In some implementations, a comment can be made on any feed item. In some implementations, comments are organized as a list explicitly tied to a particular feed tracked update, post, or status update. In some implementations, comments may not be listed in the first layer (in a hierarchal sense) of feed items, but listed as a second layer branching from a particular first layer feed item.
A “feed tracked update,” also referred to herein as a “feed update,” is one type of information update and generally refers to data representing an event. A feed tracked update can include text generated by the database system in response to the event, to be provided as one or more feed items for possible inclusion in one or more feeds. In one implementation, the data can initially be stored, and then the database system can later use the data to create text for describing the event. Both the data and/or the text can be a feed tracked update, as used herein. In various implementations, an event can be an update of a record and/or can be triggered by a specific action by a user. Which actions trigger an event can be configurable. Which events have feed tracked updates created and which feed updates are sent to which users can also be configurable. Messages and feed updates can be stored as a field or child object of the record. For example, the feed can be stored as a child object of the record.
A “group” is generally a collection of users. In some implementations, the group may be defined as users with a same or similar attribute, or by membership. In some implementations, a “group feed”, also referred to herein as a “group news feed”, includes one or more feed items about any user in the group. In some implementations, the group feed also includes information updates and other feed items that are about the group as a whole, the group's purpose, the group's description, and group records and other objects stored in association with the group. Threads of information updates including group record updates and messages, such as posts, comments, likes, etc., can define group conversations and change over time.
An “entity feed” or “record feed” generally refers to a feed of feed items about a particular record in the database, such as feed tracked updates about changes to the record and posts made by users about the record. An entity feed can be composed of any type of feed item. Such a feed can be displayed on a page such as a web page associated with the record, e.g., a home page of the record. As used herein, a “profile feed” or “user's profile feed” is a feed of feed items about a particular user. In one example, the feed items for a profile feed include posts and comments that other users make about or send to the particular user, and status updates made by the particular user. Such a profile feed can be displayed on a page associated with the particular user. In another example, feed items in a profile feed could include posts made by the particular user and feed tracked updates initiated based on actions of the particular user.
I. General Overview
Systems, apparatus, and methods are provided for implementing enterprise level social and business information networking. Such implementations can provide more efficient use of a database system. For instance, a user of a database system may not easily know when important information in the database has changed, e.g., about a project or client. Implementations can provide feed tracked updates about such changes and other events, thereby keeping users informed.
By way of example, a user can update a record in the form of a CRM object, e.g., an opportunity such as a possible sale of 1000 computers. Once the record update has been made, a feed tracked update about the record update can then automatically be provided, e.g., in a feed, to anyone subscribing to the opportunity or to the user. Thus, the user does not need to contact a manager regarding the change in the opportunity, since the feed tracked update about the update is sent via a feed right to the manager's feed page or other page.
Next, mechanisms and methods for providing systems implementing enterprise level social and business information networking will be described with reference to several implementations. First, an overview of an example of a database system is described, and then examples of tracking events for a record, actions of a user, and messages about a user or record are described. Various implementations about the data structure of feeds, customizing feeds, user selection of records and users to follow, generating feeds, and displaying feeds are also described.
II. System Overview
FIG. 1A shows a block diagram of an example of an environment 10 in which an on-demand database service can be used in accordance with some implementations. Environment 10 may include user systems 12, network 14, database system 16, processor system 17, application platform 18, network interface 20, tenant data storage 22, system data storage 24, program code 26, and process space 28. In other implementations, environment 10 may not have all of these components and/or may have other components instead of, or in addition to, those listed above.
Environment 10 is an environment in which an on-demand database service exists. User system 12 may be implemented as any computing device(s) or other data processing apparatus such as a machine or system that is used by a user to access a database system 16. For example, any of user systems 12 can be a handheld computing device, a mobile phone, a laptop computer, a work station, and/or a network of such computing devices. As illustrated in FIG. 1A (and in more detail in FIG. 1B) user systems 12 might interact via a network 14 with an on-demand database service, which is implemented in the example of FIG. 1A as database system 16.
An on-demand database service, implemented using system 16 by way of example, is a service that is made available to outside users, who do not need to necessarily be concerned with building and/or maintaining the database system. Instead, the database system may be available for their use when the users need the database system, i.e., on the demand of the users. Some on-demand database services may store information from one or more tenants into tables of a common database image to form a multi-tenant database system (MTS). A database image may include one or more database objects. A relational database management system (RDBMS) or the equivalent may execute storage and retrieval of information against the database object(s). Application platform 18 may be a framework that allows the applications of system 16 to run, such as the hardware and/or software, e.g., the operating system. In some implementations, application platform 18 enables creation, managing and executing one or more applications developed by the provider of the on-demand database service, users accessing the on-demand database service via user systems 12, or third party application developers accessing the on-demand database service via user systems 12.
The users of user systems 12 may differ in their respective capacities, and the capacity of a particular user system 12 might be entirely determined by permissions (permission levels) for the current user. For example, where a salesperson is using a particular user system 12 to interact with system 16, that user system has the capacities allotted to that salesperson. However, while an administrator is using that user system to interact with system 16, that user system has the capacities allotted to that administrator. In systems with a hierarchical role model, users at one permission level may have access to applications, data, and database information accessible by a lower permission level user, but may not have access to certain applications, database information, and data accessible by a user at a higher permission level. Thus, different users will have different capabilities with regard to accessing and modifying application and database information, depending on a user's security or permission level, also called authorization.
Network 14 is any network or combination of networks of devices that communicate with one another. For example, network 14 can be any one or any combination of a LAN (local area network), WAN (wide area network), telephone network, wireless network, point-to-point network, star network, token ring network, hub network, or other appropriate configuration. Network 14 can include a TCP/IP (Transfer Control Protocol and Internet Protocol) network, such as the global internetwork of networks often referred to as the “Internet” with a capital “I.” The Internet will be used in many of the examples herein. However, it should be understood that the networks that the present implementations might use are not so limited, although TCP/IP is a frequently implemented protocol.
User systems 12 might communicate with system 16 using TCP/IP and, at a higher network level, use other common Internet protocols to communicate, such as HTTP, FTP, AFS, WAP, etc. In an example where HTTP is used, user system 12 might include an HTTP client commonly referred to as a “browser” for sending and receiving HTTP signals to and from an HTTP server at system 16. Such an HTTP server might be implemented as the sole network interface 20 between system 16 and network 14, but other techniques might be used as well or instead. In some implementations, the network interface 20 between system 16 and network 14 includes load sharing functionality, such as round-robin HTTP request distributors to balance loads and distribute incoming HTTP requests evenly over a plurality of servers. At least for users accessing system 16, each of the plurality of servers has access to the MTS' data; however, other alternative configurations may be used instead.
In one implementation, system 16, shown in FIG. 1A, implements a web-based customer relationship management (CRM) system. For example, in one implementation, system 16 includes application servers configured to implement and execute CRM software applications as well as provide related data, code, forms, web pages and other information to and from user systems 12 and to store to, and retrieve from, a database system related data, objects, and Webpage content. With a multi-tenant system, data for multiple tenants may be stored in the same physical database object in tenant data storage 22, however, tenant data typically is arranged in the storage medium(s) of tenant data storage 22 so that data of one tenant is kept logically separate from that of other tenants so that one tenant does not have access to another tenant's data, unless such data is expressly shared. In certain implementations, system 16 implements applications other than, or in addition to, a CRM application. For example, system 16 may provide tenant access to multiple hosted (standard and custom) applications, including a CRM application. User (or third party developer) applications, which may or may not include CRM, may be supported by the application platform 18, which manages creation, storage of the applications into one or more database objects and executing of the applications in a virtual machine in the process space of the system 16.
One arrangement for elements of system 16 is shown in FIGS. 1A and 1B, including a network interface 20, application platform 18, tenant data storage 22 for tenant data 23, system data storage 24 for system data 25 accessible to system 16 and possibly multiple tenants, program code 26 for implementing various functions of system 16, and a process space 28 for executing MTS system processes and tenant-specific processes, such as running applications as part of an application hosting service. Additional processes that may execute on system 16 include database indexing processes.
Several elements in the system shown in FIG. 1A include conventional, well-known elements that are explained only briefly here. For example, each user system 12 could include a desktop personal computer, workstation, laptop, PDA, cell phone, or any wireless access protocol (WAP) enabled device or any other computing device capable of interfacing directly or indirectly to the Internet or other network connection. The term “computing device” is also referred to herein simply as a “computer”. User system 12 typically runs an HTTP client, e.g., a browsing program, such as Microsoft's Internet Explorer browser, Netscape's Navigator browser, Opera's browser, or a WAP-enabled browser in the case of a cell phone, PDA or other wireless device, or the like, allowing a user (e.g., subscriber of the multi-tenant database system) of user system 12 to access, process and view information, pages and applications available to it from system 16 over network 14. Each user system 12 also typically includes one or more user input devices, such as a keyboard, a mouse, trackball, touch pad, touch screen, pen or the like, for interacting with a graphical user interface (GUI) provided by the browser on a display (e.g., a monitor screen, LCD display, etc.) of the computing device in conjunction with pages, forms, applications and other information provided by system 16 or other systems or servers. For example, the user interface device can be used to access data and applications hosted by system 16, and to perform searches on stored data, and otherwise allow a user to interact with various GUI pages that may be presented to a user. As discussed above, implementations are suitable for use with the Internet, although other networks can be used instead of or in addition to the Internet, such as an intranet, an extranet, a virtual private network (VPN), a non-TCP/IP based network, any LAN or WAN or the like.
According to one implementation, each user system 12 and all of its components are operator configurable using applications, such as a browser, including computer code run using a central processing unit such as an Intel Pentium® processor or the like. Similarly, system 16 (and additional instances of an MTS, where more than one is present) and all of its components might be operator configurable using application(s) including computer code to run using processor system 17, which may be implemented to include a central processing unit, which may include an Intel Pentium® processor or the like, and/or multiple processor units. Non-transitory computer-readable media can have instructions stored thereon/in, that can be executed by or used to program a computing device to perform any of the methods of the implementations described herein. Computer program code 26 implementing instructions for operating and configuring system 16 to intercommunicate and to process web pages, applications and other data and media content as described herein is preferably downloadable and stored on a hard disk, but the entire program code, or portions thereof, may also be stored in any other volatile or non-volatile memory medium or device as is well known, such as a ROM or RAM, or provided on any media capable of storing program code, such as any type of rotating media including floppy disks, optical discs, digital versatile disk (DVD), compact disk (CD), microdrive, and magneto-optical disks, and magnetic or optical cards, nanosystems (including molecular memory ICs), or any other type of computer-readable medium or device suitable for storing instructions and/or data. Additionally, the entire program code, or portions thereof, may be transmitted and downloaded from a software source over a transmission medium, e.g., over the Internet, or from another server, as is well known, or transmitted over any other conventional network connection as is well known (e.g., extranet, VPN, LAN, etc.) using any communication medium and protocols (e.g., TCP/IP, HTTP, HTTPS, Ethernet, etc.) as are well known. It will also be appreciated that computer code for the disclosed implementations can be realized in any programming language that can be executed on a client system and/or server or server system such as, for example, C, C++, HTML, any other markup language, JavaTM, JavaScript, ActiveX, any other scripting language, such as VBScript, and many other programming languages as are well known may be used. (JavaTM is a trademark of Sun Microsystems, Inc.).
According to some implementations, each system 16 is configured to provide web pages, forms, applications, data and media content to user (client) systems 12 to support the access by user systems 12 as tenants of system 16. As such, system 16 provides security mechanisms to keep each tenant's data separate unless the data is shared. If more than one MTS is used, they may be located in close proximity to one another (e.g., in a server farm located in a single building or campus), or they may be distributed at locations remote from one another (e.g., one or more servers located in city A and one or more servers located in city B). As used herein, each MTS could include one or more logically and/or physically connected servers distributed locally or across one or more geographic locations. Additionally, the term “server” is meant to refer to a computing device or system, including processing hardware and process space(s), an associated storage medium such as a memory device or database, and, in some instances, a database application (e.g., OODBMS or RDBMS) as is well known in the art. It should also be understood that “server system” and “server” are often used interchangeably herein. Similarly, the database objects described herein can be implemented as single databases, a distributed database, a collection of distributed databases, a database with redundant online or offline backups or other redundancies, etc., and might include a distributed database or storage network and associated processing intelligence.
FIG. 1B shows a block diagram of an example of some implementations of elements of FIG. 1A and various possible interconnections between these elements. That is, FIG. 1B also illustrates environment 10. However, in FIG. 1B elements of system 16 and various interconnections in some implementations are further illustrated. FIG. 1B shows that user system 12 may include processor system 12A, memory system 12B, input system 12C, and output system 12D. FIG. 1B shows network 14 and system 16. FIG. 1B also shows that system 16 may include tenant data storage 22, tenant data 23, system data storage 24, system data 25, User Interface (UI) 30, Application Program Interface (API) 32, PL/SOQL 34, save routines 36, application setup mechanism 38, applications servers 1001-100N, system process space 102, tenant process spaces 104, tenant management process space 110, tenant storage space 112, user storage 114, and application metadata 116. In other implementations, environment 10 may not have the same elements as those listed above and/or may have other elements instead of, or in addition to, those listed above.
User system 12, network 14, system 16, tenant data storage 22, and system data storage 24 were discussed above in FIG. 1A. Regarding user system 12, processor system 12A may be any combination of one or more processors. Memory system 12B may be any combination of one or more memory devices, short term, and/or long term memory. Input system 12C may be any combination of input devices, such as one or more keyboards, mice, trackballs, scanners, cameras, and/or interfaces to networks. Output system 12D may be any combination of output devices, such as one or more monitors, printers, and/or interfaces to networks. As shown by FIG. 1B, system 16 may include a network interface 20 (of FIG. 1A) implemented as a set of HTTP application servers 100, an application platform 18, tenant data storage 22, and system data storage 24. Also shown is system process space 102, including individual tenant process spaces 104 and a tenant management process space 110. Each application server 100 may be configured to communicate with tenant data storage 22 and the tenant data 23 therein, and system data storage 24 and the system data 25 therein to serve requests of user systems 12. The tenant data 23 might be divided into individual tenant storage spaces 112, which can be either a physical arrangement and/or a logical arrangement of data. Within each tenant storage space 112, user storage 114 and application metadata 116 might be similarly allocated for each user. For example, a copy of a user's most recently used (MRU) items might be stored to user storage 114. Similarly, a copy of MRU items for an entire organization that is a tenant might be stored to tenant storage space 112. A UI 30 provides a user interface and an API 32 provides an application programmer interface to system 16 resident processes to users and/or developers at user systems 12. The tenant data and the system data may be stored in various databases, such as one or more Oracle databases.
Application platform 18 includes an application setup mechanism 38 that supports application developers' creation and management of applications, which may be saved as metadata into tenant data storage 22 by save routines 36 for execution by subscribers as one or more tenant process spaces 104 managed by tenant management process 110 for example. Invocations to such applications may be coded using PL/SOQL 34 that provides a programming language style interface extension to API 32. A detailed description of some PL/SOQL language implementations is discussed in commonly assigned U.S. Pat. No. 7,730,478, titled METHOD AND SYSTEM FOR ALLOWING ACCESS TO DEVELOPED APPLICATIONS VIA A MULTI-TENANT ON-DEMAND DATABASE SERVICE, by Craig Weissman, issued on Jun. 1, 2010, and hereby incorporated by reference in its entirety and for all purposes. Invocations to applications may be detected by one or more system processes, which manage retrieving application metadata 116 for the subscriber making the invocation and executing the metadata as an application in a virtual machine.
Each application server 100 may be communicably coupled to database systems, e.g., having access to system data 25 and tenant data 23, via a different network connection. For example, one application server 1001 might be coupled via the network 14 (e.g., the Internet), another application server 100N-1 might be coupled via a direct network link, and another application server 100N might be coupled by yet a different network connection. Transfer Control Protocol and Internet Protocol (TCP/IP) are typical protocols for communicating between application servers 100 and the database system. However, it will be apparent to one skilled in the art that other transport protocols may be used to optimize the system depending on the network interconnect used.
In certain implementations, each application server 100 is configured to handle requests for any user associated with any organization that is a tenant. Because it is desirable to be able to add and remove application servers from the server pool at any time for any reason, there is preferably no server affinity for a user and/or organization to a specific application server 100. In one implementation, therefore, an interface system implementing a load balancing function (e.g., an F5 Big-IP load balancer) is communicably coupled between the application servers 100 and the user systems 12 to distribute requests to the application servers 100. In one implementation, the load balancer uses a least connections algorithm to route user requests to the application servers 100. Other examples of load balancing algorithms, such as round robin and observed response time, also can be used. For example, in certain implementations, three consecutive requests from the same user could hit three different application servers 100, and three requests from different users could hit the same application server 100. In this manner, by way of example, system 16 is multi-tenant, wherein system 16 handles storage of, and access to, different objects, data and applications across disparate users and organizations.
As an example of storage, one tenant might be a company that employs a sales force where each salesperson uses system 16 to manage their sales process. Thus, a user might maintain contact data, leads data, customer follow-up data, performance data, goals and progress data, etc., all applicable to that user's personal sales process (e.g., in tenant data storage 22). In an example of a MTS arrangement, since all of the data and the applications to access, view, modify, report, transmit, calculate, etc., can be maintained and accessed by a user system having nothing more than network access, the user can manage his or her sales efforts and cycles from any of many different user systems. For example, if a salesperson is visiting a customer and the customer has Internet access in their lobby, the salesperson can obtain critical updates as to that customer while waiting for the customer to arrive in the lobby.
While each user's data might be separate from other users' data regardless of the employers of each user, some data might be organization-wide data shared or accessible by a plurality of users or all of the users for a given organization that is a tenant. Thus, there might be some data structures managed by system 16 that are allocated at the tenant level while other data structures might be managed at the user level. Because an MTS might support multiple tenants including possible competitors, the MTS should have security protocols that keep data, applications, and application use separate. Also, because many tenants may opt for access to an MTS rather than maintain their own system, redundancy, up-time, and backup are additional functions that may be implemented in the MTS. In addition to user-specific data and tenant-specific data, system 16 might also maintain system level data usable by multiple tenants or other data. Such system level data might include industry reports, news, postings, and the like that are sharable among tenants.
In certain implementations, user systems 12 (which may be client systems) communicate with application servers 100 to request and update system-level and tenant-level data from system 16 that may involve sending one or more queries to tenant data storage 22 and/or system data storage 24. System 16 (e.g., an application server 100 in system 16) automatically generates one or more SQL statements (e.g., one or more SQL queries) that are designed to access the desired information. System data storage 24 may generate query plans to access the requested data from the database.
Each database can generally be viewed as a collection of objects, such as a set of logical tables, containing data fitted into predefined categories. A “table” is one representation of a data object, and may be used herein to simplify the conceptual description of objects and custom objects according to some implementations. It should be understood that “table” and “object” may be used interchangeably herein. Each table generally contains one or more data categories logically arranged as columns or fields in a viewable schema. Each row or record of a table contains an instance of data for each category defined by the fields. For example, a CRM database may include a table that describes a customer with fields for basic contact information such as name, address, phone number, fax number, etc. Another table might describe a purchase order, including fields for information such as customer, product, sale price, date, etc. In some multi-tenant database systems, standard entity tables might be provided for use by all tenants. For CRM database applications, such standard entities might include tables for case, account, contact, lead, and opportunity data objects, each containing pre-defined fields. It should be understood that the word “entity” may also be used interchangeably herein with “object” and “table”.
In some multi-tenant database systems, tenants may be allowed to create and store custom objects, or they may be allowed to customize standard entities or objects, for example by creating custom fields for standard objects, including custom index fields. Commonly assigned U.S. Pat. No. 7,779,039, titled CUSTOM ENTITIES AND FIELDS IN A MULTI-TENANT DATABASE SYSTEM, by Weissman et al., issued on Aug. 17, 2010, and hereby incorporated by reference in its entirety and for all purposes, teaches systems and methods for creating custom objects as well as customizing standard objects in a multi-tenant database system. In certain implementations, for example, all custom entity data rows are stored in a single multi-tenant physical table, which may contain multiple logical tables per organization. It is transparent to customers that their multiple “tables” are in fact stored in one large table or that their data may be stored in the same table as the data of other customers.
FIG. 2A shows a system diagram illustrating an example of architectural components of an on-demand database service environment 200 according to some implementations. A client machine located in the cloud 204, generally referring to one or more networks in combination, as described herein, may communicate with the on-demand database service environment via one or more edge routers 208 and 212. A client machine can be any of the examples of user systems 12 described above. The edge routers may communicate with one or more core switches 220 and 224 via firewall 216. The core switches may communicate with a load balancer 228, which may distribute server load over different pods, such as the pods 240 and 244. The pods 240 and 244, which may each include one or more servers and/or other computing resources, may perform data processing and other operations used to provide on-demand services. Communication with the pods may be conducted via pod switches 232 and 236. Components of the on-demand database service environment may communicate with a database storage 256 via a database firewall 248 and a database switch 252.
As shown in FIGS. 2A and 2B, accessing an on-demand database service environment may involve communications transmitted among a variety of different hardware and/or software components. Further, the on-demand database service environment 200 is a simplified representation of an actual on-demand database service environment. For example, while only one or two devices of each type are shown in FIGS. 2A and 2B, some implementations of an on-demand database service environment may include anywhere from one to many devices of each type. Also, the on-demand database service environment need not include each device shown in FIGS. 2A and 2B, or may include additional devices not shown in FIGS. 2A and 2B.
Moreover, one or more of the devices in the on-demand database service environment 200 may be implemented on the same physical device or on different hardware. Some devices may be implemented using hardware or a combination of hardware and software. Thus, terms such as “data processing apparatus,” “machine,” “server” and “device” as used herein are not limited to a single hardware device, but rather include any hardware and software configured to provide the described functionality.
The cloud 204 is intended to refer to a data network or plurality of data networks, often including the Internet. Client machines located in the cloud 204 may communicate with the on-demand database service environment to access services provided by the on-demand database service environment. For example, client machines may access the on-demand database service environment to retrieve, store, edit, and/or process information.
In some implementations, the edge routers 208 and 212 route packets between the cloud 204 and other components of the on-demand database service environment 200. The edge routers 208 and 212 may employ the Border Gateway Protocol (BGP). The BGP is the core routing protocol of the Internet. The edge routers 208 and 212 may maintain a table of IP networks or ‘prefixes’, which designate network reachability among autonomous systems on the Internet.
In one or more implementations, the firewall 216 may protect the inner components of the on-demand database service environment 200 from Internet traffic. The firewall 216 may block, permit, or deny access to the inner components of the on-demand database service environment 200 based upon a set of rules and other criteria. The firewall 216 may act as one or more of a packet filter, an application gateway, a stateful filter, a proxy server, or any other type of firewall.
In some implementations, the core switches 220 and 224 are high-capacity switches that transfer packets within the on-demand database service environment 200. The core switches 220 and 224 may be configured as network bridges that quickly route data between different components within the on-demand database service environment. In some implementations, the use of two or more core switches 220 and 224 may provide redundancy and/or reduced latency.
In some implementations, the pods 240 and 244 may perform the core data processing and service functions provided by the on-demand database service environment. Each pod may include various types of hardware and/or software computing resources. An example of the pod architecture is discussed in greater detail with reference to FIG. 2B.
In some implementations, communication between the pods 240 and 244 may be conducted via the pod switches 232 and 236. The pod switches 232 and 236 may facilitate communication between the pods 240 and 244 and client machines located in the cloud 204, for example via core switches 220 and 224. Also, the pod switches 232 and 236 may facilitate communication between the pods 240 and 244 and the database storage 256.
In some implementations, the load balancer 228 may distribute workload between the pods 240 and 244. Balancing the on-demand service requests between the pods may assist in improving the use of resources, increasing throughput, reducing response times, and/or reducing overhead. The load balancer 228 may include multilayer switches to analyze and forward traffic.
In some implementations, access to the database storage 256 may be guarded by a database firewall 248. The database firewall 248 may act as a computer application firewall operating at the database application layer of a protocol stack. The database firewall 248 may protect the database storage 256 from application attacks such as structure query language (SQL) injection, database rootkits, and unauthorized information disclosure.
In some implementations, the database firewall 248 may include a host using one or more forms of reverse proxy services to proxy traffic before passing it to a gateway router. The database firewall 248 may inspect the contents of database traffic and block certain content or database requests. The database firewall 248 may work on the SQL application level atop the TCP/IP stack, managing applications' connection to the database or SQL management interfaces as well as intercepting and enforcing packets traveling to or from a database network or application interface.
In some implementations, communication with the database storage 256 may be conducted via the database switch 252. The multi-tenant database storage 256 may include more than one hardware and/or software components for handling database queries. Accordingly, the database switch 252 may direct database queries transmitted by other components of the on-demand database service environment (e.g., the pods 240 and 244) to the correct components within the database storage 256.
In some implementations, the database storage 256 is an on-demand database system shared by many different organizations. The on-demand database system may employ a multi-tenant approach, a virtualized approach, or any other type of database approach. An on-demand database system is discussed in greater detail with reference to FIGS. 1A and 1B.
FIG. 2B shows a system diagram further illustrating an example of architectural components of an on-demand database service environment according to some implementations. The pod 244 may be used to render services to a user of the on-demand database service environment 200. In some implementations, each pod may include a variety of servers and/or other systems. The pod 244 includes one or more content batch servers 264, content search servers 268, query servers 282, file force servers 286, access control system (ACS) servers 280, batch servers 284, and app servers 288. Also, the pod 244 includes database instances 290, quick file systems (QFS) 292, and indexers 294. In one or more implementations, some or all communication between the servers in the pod 244 may be transmitted via the switch 236.
In some implementations, the app servers 288 may include a hardware and/or software framework dedicated to the execution of procedures (e.g., programs, routines, scripts) for supporting the construction of applications provided by the on-demand database service environment 200 via the pod 244. In some implementations, the hardware and/or software framework of an app server 288 is configured to execute operations of the services described herein, including performance of the blocks of methods described with reference to FIGS. 7-10B. In alternative implementations, two or more app servers 288 may be included and cooperate to perform such methods, or one or more other servers described herein can be configured to perform the disclosed methods.
The content batch servers 264 may handle requests internal to the pod. These requests may be long-running and/or not tied to a particular customer. For example, the content batch servers 264 may handle requests related to log mining, cleanup work, and maintenance tasks.
The content search servers 268 may provide query and indexer functions. For example, the functions provided by the content search servers 268 may allow users to search through content stored in the on-demand database service environment.
The file force servers 286 may manage requests for information stored in the Fileforce storage 298. The Fileforce storage 298 may store information such as documents, images, and basic large objects (BLOBs). By managing requests for information using the file force servers 286, the image footprint on the database may be reduced.
The query servers 282 may be used to retrieve information from one or more file systems. For example, the query system 282 may receive requests for information from the app servers 288 and then transmit information queries to the NFS 296 located outside the pod.
The pod 244 may share a database instance 290 configured as a multi-tenant environment in which different organizations share access to the same database. Additionally, services rendered by the pod 244 may call upon various hardware and/or software resources. In some implementations, the ACS servers 280 may control access to data, hardware resources, or software resources.
In some implementations, the batch servers 284 may process batch jobs, which are used to run tasks at specified times. Thus, the batch servers 284 may transmit instructions to other servers, such as the app servers 288, to trigger the batch jobs.
In some implementations, the QFS 292 may be an open source file system available from Sun Microsystems® of Santa Clara, Calif. The QFS may serve as a rapid-access file system for storing and accessing information available within the pod 244. The QFS 292 may support some volume management capabilities, allowing many disks to be grouped together into a file system. File system metadata can be kept on a separate set of disks, which may be useful for streaming applications where long disk seeks cannot be tolerated. Thus, the QFS system may communicate with one or more content search servers 268 and/or indexers 294 to identify, retrieve, move, and/or update data stored in the network file systems 296 and/or other storage systems.
In some implementations, one or more query servers 282 may communicate with the NFS 296 to retrieve and/or update information stored outside of the pod 244. The NFS 296 may allow servers located in the pod 244 to access information to access files over a network in a manner similar to how local storage is accessed.
In some implementations, queries from the query servers 222 may be transmitted to the NFS 296 via the load balancer 228, which may distribute resource requests over various resources available in the on-demand database service environment. The NFS 296 may also communicate with the QFS 292 to update the information stored on the NFS 296 and/or to provide information to the QFS 292 for use by servers located within the pod 244.
In some implementations, the pod may include one or more database instances 290. The database instance 290 may transmit information to the QFS 292. When information is transmitted to the QFS, it may be available for use by servers within the pod 244 without using an additional database call.
In some implementations, database information may be transmitted to the indexer 294. Indexer 294 may provide an index of information available in the database 290 and/or QFS 292. The index information may be provided to file force servers 286 and/or the QFS 292.
III. Tracking Updates to a Record Stored in a Database
As multiple users might be able to change the data of a record, it can be useful for certain users to be notified when a record is updated. Also, even if a user does not have authority to change a record, the user still might want to know when there is an update to the record. For example, a vendor may negotiate a new price with a salesperson of company X, where the salesperson is a user associated with tenant Y. As part of creating a new invoice or for accounting purposes, the salesperson can change the price saved in the database. It may be important for co-workers to know that the price has changed. The salesperson could send an email to certain people, but this is onerous and the salesperson might not email all of the people who need to know or want to know. Accordingly, some implementations of the disclosed techniques can inform others (e.g., co-workers) who want to know about an update to a record automatically.
FIG. 3 shows a flowchart of an example of a method 300 for tracking updates to a record stored in a database system, performed in accordance with some implementations. Method 300 (and other methods described herein) may be implemented at least partially with multi-tenant database system 16, e.g., by one or more processors configured to receive or retrieve information, process the information, store results, and transmit the results. In other implementations, method 300 may be implemented at least partially with a single tenant database system. In various implementations, blocks may be omitted, combined, or split into additional blocks for method 300, as well as for other methods described herein.
In block 310, the database system receives a request to update a first record. In one implementation, the request is received from a first user. For example, a user may be accessing a page associated with the first record, and may change a displayed field and hit save. In another implementation, the database system can automatically create the request. For instance, the database system can create the request in response to another event, e.g., a request to change a field could be sent periodically at a particular date and/or time of day, or a change to another field or object. The database system can obtain a new value based on other fields of a record and/or based on parameters in the system.
The request for the update of a field of a record is an example of an event associated with the first record for which a feed tracked update may be created. In other implementations, the database system can identify other events besides updates to fields of a record. For example, an event can be a submission of approval to change a field. Such an event can also have an associated field (e.g., a field showing a status of whether a change has been submitted). Other examples of events can include creation of a record, deletion of a record, converting a record from one type to another (e.g., converting a lead to an opportunity), closing a record (e.g., a case type record), and potentially any other state change of a record—any of which could include a field change associated with the state change. Any of these events update the record whether by changing a field of the record, a state of the record, or some other characteristic or property of the record. In one implementation, a list of supported events for creating a feed tracked update can be maintained within the database system, e.g., at a server or in a database.
In block 320, the database system writes new data to the first record. In one implementation, the new data may include a new value that replaces old data. For example, a field is updated with a new value. In another implementation, the new data can be a value for a field that did not contain data before. In yet another implementation, the new data could be a flag, e.g., for a status of the record, which can be stored as a field of the record.
In some implementations, a “field” can also include records, which are child objects of the first record in a parent-child hierarchy. A field can alternatively include a pointer to a child record. A child object itself can include further fields. Thus, if a field of a child object is updated with a new value, the parent record also can be considered to have a field changed. In one example, a field could be a list of related child objects, also called a related list.
In block 330, a feed tracked update is generated about the update to the record. In one implementation, the feed tracked update is created in parts for assembling later into a display version. For example, event entries can be created and tracked in a first table, and changed field entries can be tracked in another table that is cross-referenced with the first table. More specifics of such implementations are provided later, e.g., with respect to FIG. 9A. In another implementation, the feed tracked update is automatically generated by the database system. The feed tracked update can convey in words that the first record has been updated and provide details about what was updated in the record and who performed the update. In some implementations, a feed tracked update is generated for only certain types of event and/or updates associated with the first record.
In one implementation, a tenant (e.g., through an administrator) can configure the database system to create (enable) feed tracked updates only for certain types of records. For example, an administrator can specify that records of designated types such as accounts and opportunities are enabled. When an update (or other event) is received for the enabled record type, then a feed tracked update would be generated. In another implementation, a tenant can also specify the fields of a record whose changes are to be tracked, and for which feed tracked updates are created. In one aspect, a maximum number of fields can be specified for tracking, and may include custom fields. In one implementation, the type of change can also be specified, for example, that the value change of a field is to be larger than a threshold (e.g., an absolute amount or a percentage change). In yet another implementation, a tenant can specify which events are to cause a generation of a feed tracked update. Also, in one implementation, individual users can specify configurations specific to them, which can create custom feeds as described in more detail below.
In one implementation, changes to fields of a child object are not tracked to create feed tracked updates for the parent record. In another implementation, the changes to fields of a child object can be tracked to create feed tracked updates for the parent record. For example, a child object of the parent type can be specified for tracking, and certain fields of the child object can be specified for tracking. As another example, if the child object is of a type specified for tracking, then a tracked change for the child object is propagated to parent records of the child object.
In block 340, the feed tracked update is added to a feed for the first record. In one implementation, adding the feed tracked update to a feed can include adding events to a table (which may be specific to a record or be for all or a group of objects), where a display version of a feed tracked update can be generated dynamically and presented in a GUI as a feed item when a user requests a feed for the first record. In another implementation, a display version of a feed tracked update can be added when a record feed is stored and maintained for a record. As mentioned above, a feed may be maintained for only certain records. In one implementation, the feed of a record can be stored in the database associated with the record. For example, the feed can be stored as a field (e.g., as a child object) of the record. Such a field can store a pointer to the text to be displayed for the feed tracked update.
In some implementations, only the current feed tracked update (or other current feed item) may be kept or temporarily stored, e.g., in some temporary memory structure. For example, a feed tracked update for only a most recent change to any particular field is kept. In other implementations, many previous feed tracked updates may be kept in the feed. A time and/or date for each feed tracked update can be tracked. Herein, a feed of a record is also referred to as an entity feed, as a record is an instance of a particular entity object of the database.
In block 350, followers of the first record can be identified. A follower is a user following the first record, such as a subscriber to the feed of the first record. In one implementation, when a user requests a feed of a particular record, such an identification of block 350 can be omitted. In another implementation where a record feed is pushed to a user (e.g., as part of a news feed), then the user can be identified as a follower of the first record. Accordingly, this block can include the identification of records and other objects being followed by a particular user.
In one implementation, the database system can store a list of the followers for a particular record. In various implementations, the list can be stored with the first record or associated with the record using an identifier (e.g., a pointer) to retrieve the list. For example, the list can be stored in a field of the first record. In another implementation, a list of the records that a user is following is used. In one implementation, the database system can have a routine that runs for each user, where the routine polls the records in the list to determine if a new feed tracked update has been added to a feed of the record. In another implementation, the routine for the user can be running at least partially on a user device, which contacts the database to perform the polling.
In block 360, in one implementation, the feed tracked update can be stored in a table, as described in greater detail below. When the user opens a feed, an appropriate query is sent to one or more tables to retrieve updates to records, also described in greater detail below. In some implementations, the feed shows feed tracked updates in reverse chronological order. In one implementation, the feed tracked update is pushed to the feed of a user, e.g., by a routine that determines the followers for the record from a list associated with the record. In another implementation, the feed tracked update is pulled to a feed, e.g., by a user device. This pulling may occur when a user requests the feed, as occurs in block 370. Thus, these actions may occur in a different order. The creation of the feed for a pull may be a dynamic creation that identifies records being followed by the requesting user, generates the display version of relevant feed tracked updates from stored information (e.g., event and field change), and adds the feed tracked updates into the feed. A feed of feed tracked updates of records and other objects that a user is following is also generally referred to herein as a news feed, which can be a subset of a larger information feed in which other types of information updates appear, such as posts.
In yet another implementation, the feed tracked update could be sent as an email to the follower, instead of in a feed. In one implementation, email alerts for events can enable people to be emailed when certain events occur. In another implementation, emails can be sent when there are posts on a user profile and posts on entities to which the user subscribes. In one implementation, a user can turn on/off email alerts for all or some events. In an implementation, a user can specify what kind of feed tracked updates to receive about a record that the user is following. For example, a user can choose to only receive feed tracked updates about certain fields of a record that the user is following, and potentially about what kind of update was performed (e.g., a new value input into a specified field, or the creation of a new field).
In block 370, a follower can access his/her news feed to see the feed tracked update. In one implementation, the user has just one news feed for all of the records that the user is following. In one aspect, a user can access his/her feed by selecting a particular tab or other object on a page of an interface to the database system. Once selected the feed can be provided as a list, e.g., with an identifier (e.g., a time) or including some or all of the text of the feed tracked update. In another implementation, the user can specify how the feed tracked updates are to be displayed and/or sent to the user. For example, a user can specify a font for the text, a location of where the feed can be selected and displayed, amount of text to be displayed, and other text or symbols to be displayed (e.g., importance flags).
FIG. 4 shows a block diagram of an example of components of a database system configuration 400 performing a method for tracking an update to a record according to some implementations. Database system configuration 400 can perform implementations of method 300, as well as implementations of other methods described herein.
A first user 405 sends a request 1 to update record 425 in database system 416. Although an update request is described, other events that are being tracked are equally applicable. In various implementations, the request 1 can be sent via a user interface (e.g., 30 of FIG. 1B) or an application program interface (e.g., API 32). An I/O port 420 can accommodate the signals of request 1 via any input interface, and send the signals to one or more processors 417. The processor 417 can analyze the request and determine operations to be performed. Herein, any reference to a processor 417 can refer to a specific processor or any set of processors in database system 416, which can be collectively referred to as processor 417.
Processor 417 can determine an identifier for record 425, and send commands with the new data 2 of the request to record database 412 to update record 425. In one implementation, record database 412 is where tenant storage space 112 of FIG. 1B is located. The request 1 and new data commands 2 can be encapsulated in a single write transaction sent to record database 412. In one implementation, multiple changes to records in the database can be made in a single write transaction.
Processor 417 can also analyze request 1 to determine whether a feed tracked update is to be created, which at this point may include determining whether the event (e.g., a change to a particular field) is to be tracked. This determination can be based on an interaction (i.e., an exchange of data) with record database 412 and/or other databases, or based on information stored locally (e.g., in cache or RAM) at processor 417. In one implementation, a list of record types that are being tracked can be stored. The list may be different for each tenant, e.g., as each tenant may configure the database system to its own specifications. Thus, if the record 425 is of a type not being tracked, then the determination of whether to create a feed tracked update can stop there.
The same list or a second list (which can be stored in a same location or a different location) can also include the fields and/or events that are tracked for the record types in the first list. This list can be searched to determine if the event is being tracked. A list may also contain information having the granularity of listing specific records that are to be tracked (e.g., if a tenant can specify the particular records to be tracked, as opposed to just type).
As an example, processor 417 may obtain an identifier associated with record 425 (e.g., obtained from request 1 or database 412), potentially along with a tenant identifier, and cross-reference the identifier with a list of records for which feed tracked updates are to be created. Specifically, the record identifier can be used to determine the record type and a list of tracked types can be searched for a match. The specific record may also be checked if such individual record tracking was enabled. The name of the field to be changed can also be used to search a list of tracking-enabled fields. Other criteria besides field and events can be used to determine whether a feed tracked update is created, e.g., type of change in the field. If a feed tracked update is to be generated, processor 417 can then generate the feed tracked update.
In some implementations, a feed tracked update is created dynamically when a feed (e.g., the entity feed of record 425) is requested. Thus, in one implementation, a feed tracked update can be created when a user requests the entity feed for record 425. In this implementation, the feed tracked update may be created (e.g., assembled), including re-created, each time the entity feed is to be displayed to any user. In one implementation, one or more event history tables can keep track of previous events so that the feed tracked update can be re-created.
In another implementation, a feed tracked update can be created at the time the event occurs, and the feed tracked update can be added to a list of feed items. The list of feed items may be specific to record 425, or may be an aggregate of feed items including feed items for many records. Such an aggregate list can include a record identifier so that the feed items for the entity feed of record 425 can be easily retrieved. For example, after the feed tracked update has been generated, processor 417 can add the new feed tracked update 3 to a feed of record 425. As mentioned above, in one implementation, the feed can be stored in a field (e.g., as a child object) of record 425. In another implementation, the feed can be stored in another location or in another database, but with a link (e.g., a connecting identifier) to record 425. The feed can be organized in various ways, e.g., as a linked list, an array, or other data structure.
A second user 430 can access the new feed tracked update 3 in various ways. In one implementation, second user 430 can send a request 4 for the record feed. For example, second user 430 can access a home page (detail page) of the record 425 (e.g., with a query or by browsing), and the feed can be obtained through a tab, button, or other activation object on the page. The feed can be displayed on the screen or downloaded.
In another implementation, processor 417 can add the new feed tracked update 5 to a feed (e.g., a news feed) of a user that is following record 425. In one implementation, processor 417 can determine each of the followers of record 425 by accessing a list of the users that have been registered as followers. This determination can be done for each new event (e.g., update 1). In another implementation, processor 417 can poll (e.g., with a query) the records that second user 430 is following to determine when new feed tracked updates (or other feed items) are available. Processor 417 can use a follower profile 435 of second user 430 that can contain a list of the records that the second user 430 is following. Such a list can be contained in other parts of the database as well. Second user 430 can then send a request 6 to his/her profile 435 to obtain a feed, which contains the new feed tracked update. The user's profile 435 can be stored in a profile database 414, which can be the same or different than database 412.
In some implementations, a user can define a news feed to include new feed tracked updates from various records, which may be limited to a maximum number. In one implementation, each user has one news feed. In another implementation, the follower profile 435 can include the specifications of each of the records to be followed (with the criteria for what feed tracked updates are to be provided and how they are displayed), as well as the feed.
Some implementations can provide various types of record (entity) feeds. Entity Feeds can exist for record types like account, opportunity, case, and contact. An entity feed can tell a user about the actions that people have taken on that particular record or on one its related records. The entity feed can include who made the action, which field was changed, and the old and new values. In one implementation, entity feeds can exist on all supported records as a list that is linked to the specific record. For example, a feed could be stored in a field that allows lists (e.g., linked lists) or as a child object.
IV. Tracking Actions of a User
In addition to knowing about events associated with a particular record, it can be helpful for a user to know what a particular user is doing. In particular, it might be nice to know what the user is doing without the user having to generate the feed tracked update (e.g., a user submitting a synopsis of what the user has done). Accordingly, implementations can automatically track actions of a user that trigger events, and feed tracked updates can be generated for certain events.
FIG. 5 shows a flowchart of an example of a method 500 for tracking actions of a user of a database system, performed in accordance with some implementations. Method 500 may be performed in addition to method 300. The operations of method 300, including order of blocks, can be performed in conjunction with method 500 and other methods described herein. Thus, a feed can be composed of changes to a record and actions of users.
In block 510, a database system (e.g., 16 of FIGS. 1A and 1B) identifies an action of a first user. In one implementation, the action triggers an event, and the event is identified. For example, the action of a user requesting an update to a record can be identified, where the event is receiving a request or is the resulting update of a record. The action may thus be defined by the resulting event. In another implementation, only certain types of actions (events) are identified. Which actions are identified can be set as a default or can be configurable by a tenant or even configurable at a user level. In this way, processing effort can be reduced since only some actions are identified.
In block 520, it is determined whether the event qualifies for a feed tracked update. In one implementation, a predefined list of events (e.g., as mentioned herein) can be created so that only certain actions are identified. In one implementation, an administrator (or other user) of a tenant can specify the type of actions (events) for which a feed tracked update is to be generated. This block may also be performed for method 300.
In block 530, a feed tracked update is generated about the action. In an example where the action is an update of a record, the feed tracked update can be similar or the same as the feed tracked update created for the record. The description can be altered though to focus on the user as opposed to the record. For example, “John D. has closed a new opportunity for account XYZ” as opposed to “an opportunity has been closed for account XYZ.”
In block 540, the feed tracked update is added to a profile feed of the first user when, e.g., the user clicks on a tab to open a page in a browser program displaying the feed. In one implementation, a feed for a particular user can be accessed on a page of the user's profile, in a similar manner as a record feed can be accessed on a detail page of the record. In another implementation, the first user may not have a profile feed and the feed tracked update may just be stored temporarily before proceeding. A profile feed of a user can be stored associated with the user's profile. This profile feed can be added to a news feed of another user.
In block 550, followers of the first user are identified. In one implementation, a user can specify which type of actions other users can follow. Similarly, in one implementation, a follower can select what actions by a user the follower wants to follow. In an implementation where different followers follow different types of actions, which users are followers of that user and the particular action can be identified, e.g., using various lists that track what actions and criteria are being followed by a particular user. In various implementations, the followers of the first user can be identified in a similar manner as followers of a record, as described above for block 350.
In block 560, the feed tracked update is added to a news feed of each follower of the first user when, e.g., the follower clicks on a tab to open a page displaying the news feed. The feed tracked update can be added in a similar manner as the feed items for a record feed. The news feed can contain feed tracked updates both about users and records. In another implementation, a user can specify what kind of feed tracked updates to receive about a user that the user is following. For example, a user could specify feed tracked updates with particular keywords, of certain types of records, of records owned or created by certain users, particular fields, and other criteria as mentioned herein.
In block 570, a follower accesses the news feed and sees the feed tracked update. In one implementation, the user has just one news feed for all of the records that the user is following. In another implementation, a user can access his/her own feed (i.e. feed about his/her own actions) by selecting a particular tab or other object on a page of an interface to the database system. Thus, a feed can include feed tracked updates about what other users are doing in the database system. When a user becomes aware of a relevant action of another user, the user can contact the co-worker, thereby fostering teamwork.
V. Generation of a Feed Tracked Update
As described above, some implementations can generate text describing events (e.g., updates) that have occurred for a record and actions by a user that trigger an event. A database system can be configured to generate the feed tracked updates for various events in various ways.
In one implementation, the feed tracked update is a grammatical sentence, thereby being easily understandable by a person. In another implementation, the feed tracked update provides detailed information about the update. In various examples, an old value and new value for a field may be included in the feed tracked update, an action for the update may be provided (e.g., submitted for approval), and the names of particular users that are responsible for replying or acting on the feed tracked update may be also provided. The feed tracked update can also have a level of importance based on settings chosen by the administrator, a particular user requesting an update, or by a following user who is to receive the feed tracked update, which fields is updated, a percentage of the change in a field, the type of event, or any combination of these factors.
The system may have a set of heuristics for creating a feed tracked update from the event (e.g., a request to update). For example, the subject may be the user, the record, or a field being added or changed. The verb can be based on the action requested by the user, which can be selected from a list of verbs (which may be provided as defaults or input by an administrator of a tenant). In one implementation, feed tracked updates can be generic containers with formatting restrictions.
As an example of a feed tracked update for a creation of a new record, “Mark Abramowitz created a new Opportunity for IBM—20,000 laptops with Amount as $3.5M and Sam Palmisano as Decision Maker.” This event can be posted to the profile feed for Mark Abramowitz and the entity feed for record of Opportunity for IBM-20,000 laptops. The pattern can be given by (AgentFullName) created a new (ObjectName)(RecordName) with [(FieldName) as (FieldValue) [,/and] ]*[[added/changed/removed] (RelatedListRecordName) [as/to/as] (RelatedListRecordValue) [,/and] ]*. Similar patterns can be formed for a changed field (standard or custom) and an added child record to a related list.
VI. Tracking Commentary from or about a User
Some implementations can also have a user submit text, instead of the database system generating a feed tracked update. As the text is submitted as part or all of a message by a user, the text can be about any topic. Thus, more information than just actions of a user and events of a record can be conveyed. In one implementation, the messages can be used to ask a question about a particular record, and users following the record can provide comments and responses.
FIG. 6 shows a flowchart of an example of a method 600 for creating a news feed from messages created by a user about a record or another user, performed in accordance with some implementations. In one implementation, method 600 can be combined with methods 300 and 500. In one aspect, a message can be associated with the first user when the first user creates the message (e.g., a post or comment about a record or another user). In another aspect, a message can be associated with the first user when the message is about the first user (e.g., posted by another user on the first user's profile feed).
In block 610, the database system receives a message (e.g., a post or status update) associated with a first user. The message (e.g., a post or status update) can contain text and/or multimedia content submitted by another user or by the first user. In one implementation, a post is for a section of the first user's profile page where any user can add a post, and where multiple posts can exist. Thus, a post can appear on the first user's profile page and can be viewed when the first user's profile is visited. For a message about a record, the post can appear on a detail page of a record. Note the message can appear in other feeds as well. In another implementation, a status update about the first user can only be added by the first user. In one implementation, a user can only have one status message.
In block 620, the message is added to a table, as described in greater detail below. When the feed is opened, a query filters one or more tables to identify the first user, identify other persons that the user is following, and retrieve the message. Messages and record updates are presented in a combined list as the feed. In this way, in one implementation, the message can be added to a profile feed of the first user, which is associated (e.g., as a related list) with the first user's profile. In one implementation, the posts are listed indefinitely. In another implementation, only the most recent posts (e.g., last 50) are kept in the profile feed. Such implementations can also be employed with feed tracked updates. In yet another implementation, the message can be added to a profile of the user adding the message.
In block 630, the database system identifies followers of the first user. In one implementation, the database system can identify the followers as described above for method 500. In various implementations, a follower can select to follow a feed about the actions of the first user, messages about the first user, or both (potentially in a same feed).
In block 640, the message is added to a news feed of each follower. In one implementation, the message is only added to a news feed of a particular follower if the message matches some criteria, e.g., the message includes a particular keyword or other criteria. In another implementation, a message can be deleted by the user who created the message. In one implementation, once deleted by the author, the message is deleted from all feeds to which the message had been added.
In block 650, the follower accesses a news feed and sees the message. For example, the follower can access a news feed on the follower's own profile page. As another example, the follower can have a news feed sent to his/her own desktop without having to first go to a home page.
In block 660, the database system receives a comment about the message. The database system can add the comment to a feed of the same first user, much as the original message was added. In one implementation, the comment can also be added to a feed of a second user who added the comment. In one implementation, users can also reply to the comment. In another implementation, users can add comments to a feed tracked update, and further comments can be associated with the feed tracked update. In yet another implementation, making a comment or message is not an action to which a feed tracked update is created. Thus, the message may be the only feed item created from such an action.
In one implementation, if a feed tracked update or post is deleted, its corresponding comments are deleted as well. In another implementation, new comments on a feed tracked update or post do not update the feed tracked update timestamp. Also, the feed tracked update or post can continue to be shown in a feed (profile feed, record feed, or news feed) if it has had a comment within a specified timeframe (e.g., within the last week). Otherwise, the feed tracked update or post can be removed in an implementation.
In some implementations, all or most feed tracked updates can be commented on. In other implementations, feed tracked updates for certain records (e.g., cases or ideas) are not commentable. In various implementations, comments can be made for any one or more records of opportunities, accounts, contacts, leads, and custom objects.
In block 670, the comment is added to a news feed of each follower. In one implementation, a user can make the comment within the user's news feed. Such a comment can propagate to the appropriate profile feed or record feed, and then to the news feeds of the following users. Thus, feeds can include what people are saying, as well as what they are doing. In one aspect, feeds are a way to stay up-to-date (e.g., on users, opportunities, etc.) as well as an opportunity to reach out to co-workers/partners and engage them around common goals.
In some implementations, users can rate feed tracked updates or messages (including comments). A user can choose to prioritize a display of a feed so that higher rated feed items show up higher on a display. For example, in an implementation where comments are answers to a specific question, users can rate the different status posts so that a best answer can be identified. As another example, users are able to quickly identify feed items that are most important as those feed items can be displayed at a top of a list. The order of the feed items can be based on an importance level (which can be determined by the database system using various factors, some of which are mentioned herein) and based on a rating from users. In one implementation, the rating is on a scale that includes at least 3 values. In another implementation, the rating is based on a binary scale.
Besides a profile for a user, a group can also be created. In various implementations, the group can be created based on certain attributes that are common to the users, can be created by inviting users, and/or can be created by receiving requests to join from a user. In one implementation, a group feed can be created, with messages being added to the group feed when someone submits a message to the group as a whole through a suitable user interface. For example, a group page may have a group feed or a section within the feed for posts, and a user can submit a post through a publisher component in the user interface by clicking on a “Share” or similar button. In another implementation, a message can be added to a group feed when the message is submitted about any one of the members. Also, a group feed can include feed tracked updates about actions of the group as a whole (e.g., when an administrator changes data in a group profile or a record owned by the group), or about actions of an individual member.
VII. Grouping Similar Labels Using Collaborative Filtering
FIG. 7 shows a flowchart of an example of a computer implemented method 700 for identifying similar labels to a user, performed in accordance with some implementations.
In FIG. 7, at block 710, a server maintains a plurality of data entries in one or more database tables storing textual data. Each data entry of a first portion of the data entries includes: a text sequence, a label, and a text-to-label association score. The text-to-label association score may indicate a number of times that the text sequence appears in previous incoming texts associated with the label. Each data entry of a second portion of the data entries includes: a first label, a second label, and a similarity score.
In some implementations, the text sequence may be a word or a pair of words. As an example, in some foreign languages, pairs of words are the operative unit of language, in which case those pairs of words may be the text sequences that are stored in the first and second database tables.
In some implementations, an incoming text may be one of: a social media message, a text document, a CRM object including textual data, a feed item, an article, or any object that may be labeled and has text associated with it.
In some implementations, a label is a user-generated topic assigned to an incoming text. In another implementation, a label may be a hashtag applied to a social media message.
In some implementations, a data entry of the first portion of the data entries may include a text sequence, a label, and a score. For every text-label pair, a first database table may contain a single data entry for that pair. The score associated with the text-label pair in the data entry may represent how often the text sequence has been included in social media messages to which the label was assigned. For example, a relatively high score for a text-label pair such as “senator”-“politics” would suggest that the word “senator” appeared in social media messages associated with the label “politics” fairly frequently. In implementations where an inflation factor is applied to weight the more recent topic assignments, further described below, a higher score for a text-label pair may reflect that the label was assigned to a social media message containing the word relatively recently. In other implementations, the score may simply be a count of the number of times that the text sequence appears in previous incoming texts to which the label has been assigned.
FIG. 10A shows an example of a database table 1000 identifying text-to-label association scores for a text sequence and a label, according to some implementations. Table 1000 of FIG. 10A is structured with a text sequence column 1004, a label column 1014, and a text-to-label score column 1024 identifying a score indicating how often the text sequence appears in social media messages having the associated label. The text-to-label score associated with a text sequence and a label may be incremented every time the text sequence appears in a social media message to which the label is assigned. When a label is assigned to a social media message containing a text sequence, and a data entry containing the label and the text sequence does not exist in table 1000, a new data entry may be created in table 1000 containing the label and the text sequence. The text-to-label scores of the first 1002, second 1012, and third 1022 data entries of table 1000 indicate that the text sequence “President” appears in incoming texts associated with the label “politics” more frequently than does the text sequence “senator” appear in incoming texts associated with the label “political.” They also indicate that the text sequence “senator” in turn appears in incoming texts associated with the label “political” more frequently than it does in incoming texts associated with the label “politics.”
In FIG. 7, at block 720, the server performing method 700 analyzes the data of the first portion of data entries to generate one or more pairs, each pair including information identifying a first label and a second label. In some implementations, the server may generate every pairwise combination of labels in the first portion of data entries. In other implementations, the server may generate every pairwise combination of the labels that have a minimum combined text-to-label score. Because there may be many user-generated labels represented in the first portion of data entries, the server may generate pairs for just the more frequently used labels, rather than for any label that has ever been used.
In FIG. 7, at block 730, the server performing method 700 calculates a similarity score for each of the one or more pairs. In some implementations, the similarity score is generated by collaborative filtering methods used on the one or more pairs generated by the server. The similarity score may be a percentage score indicating the similarity between the first label of the pair and the second label of the pair.
In some implementations, the similarity score of a pair including a first label and a second label is based at least in part on a frequency of one or more text sequences appearing in a first one or more previous incoming texts associated the first label and in a second one or more previous incoming texts associated with the second label.
In some implementations, the similarity score of a pair is normalized for a frequency at which the text sequences associated with the labels are used in incoming texts. For example, a text sequence may be commonly used among all incoming texts of an on-demand service environment. This may cause certain pairs having this text sequence to have higher similarity scores because of the higher prevalence of the common text sequence. In these implementations, the similarity scores may be normalized using the frequency with which the text sequence appears in incoming texts of the system.
In some implementations, calculating the similarity score for a pair comprises calculating a cosine similarity score for the first and second labels of the pair using a first vector of text sequences associated with the first label and a second vector of text sequences associated with the second label, wherein a text sequence is associated with a label if it appears in a previous incoming text associated with the label.
In some implementations, the first vector of text sequences may include all of the text sequences that have appeared in any previous incoming text to which the first label has been applied. The second vector of text sequences may include all of the text sequences that have appeared in any previous incoming text to which the second label has been applied. The calculated similarity score represents how similar the two sets of text sequences corresponding to the first and second labels are.
In some implementations, calculating the similarity score for each of the one or more pairs comprises using a collaborative filtering method to determine cosine similarity scores for the pairs, and wherein the second portion of the data entries is a collaborative filter table having the one or more pairs recorded therein based on the analysis. Basic collaborative filtering methods will be understood by a person skilled in the art. As is described herein, a collaborative filtering method relates all pairs of items in a system by how many times the two items have been accessed by the same user. For example, when a user accesses item 1, the method may predict that such a user will also access item 2 if the two items are evaluated to be highly related based on the behavior of other users within the system. That is to say, based on other users within the system having often accessed both item 1 and item 2 together, the collaborative filtering method may then make a determination that these two exemplary items exhibit high correlation, and thus, the user in question may be “predicted” (e.g., is considered “more likely”) to access item 2 after being observed to have accessed item 1. In the disclosed implementations, “item 1” may be a first label, and “item 2” may be a second label. Some of the disclosed implementations generate the one or more pairs, which provide text sequence vectors based on the first and second labels and the text sequences that appear in incoming texts associated with the first and second labels stored in the first portion of data entries to serve as input into collaborative filtering methods to generate similarity scores for the one or more pairs.
In FIG. 7, at block 740, the server performing method 700 stores each of the one or more pairs and the respective similarity scores in the second portion of the data entries. The second portion of the data entries may include a first label field, a second label field, and a CF similarity score percentage field.
FIG. 10B shows an example of a database table 1050 identifying similarity scores for a first label and a second label, according to some implementations. The table includes a first label field 1054, a second label field 1064, and a CF similarity score percentage field 1074. While these fields are presented as single columns in a single table, other implementations may provide this data as multiple columns in multiple tables in multiple databases as well. The table includes a first pair 1052, which demonstrates a 87% CF similarity score for the “politics” label and the “political” label. This may indicate that incoming texts that were given the label “politics” contain a relatively high number of text sequences in common with incoming texts that were given the label “political.” The second pair 1062 demonstrates a 9% CF similarity score for the labels “sports” and “politics.” The third pair 1072 demonstrates a 43% CF similarity score for the labels “sports” and “NBA.” In this example, the first pair of labels 1052 is more similar than the second 1062 and third 1072 pairs of labels. This determination may be used to recommend that the first pair of labels, “politics” and “political” be merged in the system.
FIG. 8 shows a flowchart of an example of a computer implemented method 800 for identifying similar labels to a user, performed in accordance with some implementations.
At block 810 of FIG. 8, a server performing method 800 maintains a plurality of data entries in one or more database tables storing textual data, as generally described above at block 710 of FIG. 7.
At block 820 of FIG. 8, the server performing method 800 analyzes the data of the first portion of data entries to generate one or more pairs, each pair including information identifying a first label and a second label, as generally described above at block 720 of FIG. 7.
At block 832 of FIG. 8, the server performing method 800 calculates a cosine similarity score for the first and second labels of the pair using a first vector of text sequences associated with the first label and a second vector of text sequences associated with the second label. In some implementations, a text sequence is associated with a label if it appears in a previous incoming text associated with the label. The first and second vectors of text sequences are generally described above at block 730 of FIG. 7.
At block 840 of FIG. 8, the server performing method 800 stores each of the one or more pairs and the respective similarity scores in the second portion of the data entries, as generally described above at block 740 of FIG. 7.
At block 850 of FIG. 8, the server performing method 800 receives a request for labels having similar associated text sequences. The request may be generated by a computing device in response to a request to de-duplicate topics or to identify similar topics in an on-demand service environment. The request may also be generated by a computing device in response to a request to identify topics that are similar to a specified topic, based on the contents of social media messages to which the topics are assigned. Alternatively, the request may be generated by a computing device in response to a request to identify pairs of topics that have a designated level of similarity, based on the contents of the social media messages to which the topics are assigned.
At block 852 of FIG. 8, the server performing method 800 identifies, based on the stored one or more pairs and the stored similarity scores, a set of pairs having the same first label. As an example, the server may identify all of the pairs that have “politics” as the first label. Each of the identified pairs includes a second label and its similarity to the “politics” label, the degree of similarity being specified by the CF similarity score percentage of data entry. In the example of FIG. 10B, the first label may be “sports,” and pairs 1062 and 1072 may be identified, where the label “politics” has a 9% CF similarity score compared to the “sports” label, and the label “NBA” has a 43% CF similarity score compared to the “sports” label.
At block 854 of FIG. 8, the server performing method 800 selects a pair of the identified set of pairs, wherein the respective similarity score of the selected pair is higher than that of the other pairs of the identified set of pairs. In the example of FIG. 10B, between pairs 1062 and 1072, the server may select pair 1072 as having a higher similarity score than pair 1062, indicating that the labels “sports” and “NBA” are more similar than the labels “sports” and “politics.”
At block 856 of FIG. 8, the server performing method 800 determines that the similarity score of the selected pair is higher than a designated similarity threshold. In some implementations, the server may designate a similarity percentage threshold that must be met before a pair of labels is recommended to a user as being similar. In the example of FIG. 10B, the designated similarity threshold may be 67%. In that case, neither the second pair 1062 nor the third pair 1072 may be transmitted to the user as a similar pair of labels, even though the third pair 1072 is more similar than the second pair 1062. Alternatively, if the designated similarity threshold is, say, 40%, then the server may determine that pair 1072 has a similarity score that is higher than the similarity threshold.
At block 858 of FIG. 8, the server performing method 800 transmits the first label and the second label of the selected pair to the computing device as a similar pair of labels. In the example of FIG. 10B, where the designated similarity threshold is 40%, the third pair 1072 may be transmitted to the computing device as a similar pair of labels.
Blocks 852, 854, 856, and 858 of FIG. 8 may be performed again with a different label as the first label. For example, in FIG. 10B, all of the pairs having “politics” as the first label may be identified, and their similarity scores may be compared to select the pair having the highest similarity. For example, the first pair “politics” and “political” has a relatively high similarity score and may be identified as a possible pair of labels that may be merged or de-duplicated.
In some implementations, the selected pair of similar labels is transmitted to the computing device with one or more other pairs of similar labels. As an example, instead of just the most similar pair of labels being transmitted to the computing device, the top three (or other number) similar pairs of labels may be transmitted to the computing device, so long as their similarity scores are higher than the designated threshold similarity score.
FIG. 9 shows a flowchart of an example of a computer implemented method 900 for identifying similar labels to a user, performed in accordance with some implementations.
At block 910 of FIG. 9, a server performing method 900 maintains a plurality of data entries in one or more database tables storing textual data, as generally described above at block 710 of FIG. 7.
At block 920 of FIG. 9, the server performing method 900 analyzes the data of the first portion of data entries to generate one or more pairs, each pair including information identifying a first label and a second label, as generally described above at block 720 of FIG. 7.
At block 934 of FIG. 9, the server performing method 900 uses a collaborative filtering method to determine cosine similarity scores for the pairs, wherein the second portion of the data entries is a collaborative filter table having the one or more pairs recorded therein based on the analysis, as generally described above at block 730 of FIG. 7.
At block 940 of FIG. 9, the server performing method 900 stores each of the one or more pairs and the respective similarity scores in the second portion of the data entries, as generally described above at block 740 of FIG. 7.
At block 960 of FIG. 9, the server performing method 900 periodically identifies one or more incoming texts and associated labels, wherein each incoming text includes one or more text sequences. In some implementations, the server may identify the one or more incoming texts and associated labels weekly. In other implementations, this may occur nightly or hourly.
At block 962 of FIG. 9, the server performing method 900, for each incoming text and associated label, updates the first portion of data entries with the one or more text sequences of the incoming text and the associated label. Turning to FIG. 10A, the text-to-label scores of column 1024 may be updated as their associated text sequences appear in incoming texts to which their associated label is applied. For example, if one of the incoming texts is a social media message that includes the word “senator” and carries the label “politics,” the text-to-label score of row 1002 may be incremented by an inflation factor, as described below in block 966 of FIG. 9.
At block 964 of FIG. 9, the server performing method 900 identifies or creates a first data entry of the first portion of data entries that includes the text sequence and the requested label. In the case that the incoming text with a particular label includes a text sequence, and no text sequence-label pair having the particular label and included text sequence exists in table 1000 of FIG. 10A, a new entry may be created in FIG. 10A including the particular label and the included text sequence. In some implementations, the text-to-label score may begin at zero and be incremented by the server. In the case that the text sequence-label pair does appear in table 1000, then the pair may be identified by the server, rather than creating a new entry corresponding to the pair.
At block 966 of FIG. 9, the server performing method 900 increments the text-to-label association score of the identified or created first data entry by an inflation factor.
For example, an inflation factor may be applied when the scores are incremented in order to weight more recent label associations more heavily relative to label associations that happened in the past and to compensate for the passage of time among associations of the label to incoming texts containing the text sequence. In these implementations, before incrementing the text-to-label score, the amount by which the score is to be incremented can be time inflated with a first value of an inflation factor. By the same token, when the text-to-label score is later retrieved to determine the relevance of the topic to social media messages containing the word, the text-to-label score can again be normalized with a later second value of the inflation factor. Thus, the inflation factor can be implemented to have a changing value as a function of time. In this way, more recent label associations can have a higher value, indicating higher relevance, than older label associations in terms of text-to-label scores. In some implementations, the inflation factor has a value based on a measure of time. In other implementations, the inflation factor is an exponential function of a measure of time.
In some implementations, where no inflation factor is utilized to weight more recent associations, normalizing the text-to-label association score may comprise dividing the score by one.
In some implementations, blocks 962, 964, and 966 may be performed responsive to a request to associate a label with an incoming text. As an example, a user may apply a label to a incoming text that the user has composed. At this point, the server may update the first portion of data entries with the one or more text sequences of the social media message and the applied label.
In some of the disclosed implementations, users of an on-demand service environment may be working on the same text document and may separately upload to the servers different versions of the same text document with different file names. In these cases, the incoming texts may be the uploaded text documents, wherein the label is the file name of the text document, and wherein the similarity scores identify text documents that are different versions of the same original text document. The disclosed implementations may also identify these different text documents that are different versions of the same document but have different file names.
The specific details of the specific aspects of implementations disclosed herein may be combined in any suitable manner without departing from the spirit and scope of the disclosed implementations. However, other implementations may be directed to specific implementations relating to each individual aspect, or specific combinations of these individual aspects.
While the disclosed examples are often described herein with reference to an implementation in which an on-demand database service environment is implemented in a system having an application server providing a front end for an on-demand database service capable of supporting multiple tenants, the present implementations are not limited to multi-tenant databases nor deployment on application servers. Implementations may be practiced using other database architectures, i.e., ORACLE®, DB2® by IBM and the like without departing from the scope of the implementations claimed.
It should be understood that some of the disclosed implementations can be embodied in the form of control logic using hardware and/or using computer software in a modular or integrated manner. Other ways and/or methods are possible using hardware and a combination of hardware and software.
Any of the software components or functions described in this application may be implemented as software code to be executed by a processor using any suitable computer language such as, for example, Java, C++ or Perl using, for example, conventional or object-oriented techniques. The software code may be stored as a series of instructions or commands on a computer-readable medium for storage and/or transmission, suitable media include random access memory (RAM), a read only memory (ROM), a magnetic medium such as a hard-drive or a floppy disk, or an optical medium such as a compact disk (CD) or DVD (digital versatile disk), flash memory, and the like. The computer-readable medium may be any combination of such storage or transmission devices. Computer-readable media encoded with the software/program code may be packaged with a compatible device or provided separately from other devices (e.g., via Internet download). Any such computer-readable medium may reside on or within a single computing device or an entire computer system, and may be among other computer-readable media within a system or network. A computer system, or other computing device, may include a monitor, printer, or other suitable display for providing any of the results mentioned herein to a user.
While various implementations have been described herein, it should be understood that they have been presented by way of example only, and not limitation. Thus, the breadth and scope of the present application should not be limited by any of the implementations described herein, but should be defined only in accordance with the following and later-submitted claims and their equivalents.

## Claims

1. A system for identifying similar labels, the system comprising:
a database system implemented using a server system comprising one or more hardware processors, the database system configurable to cause:
maintaining, through one or more databases, a plurality of data entries, each data entry of a first portion of the data entries identifying: a text sequence, a label, and a text-to-label association score indicating a number of times that the text sequence appears in one or more previous incoming texts associated with the label, and each data entry of a second portion of the data entries identifying: a first label, a second label, and a similarity score;
generating a plurality of pairs based on the first portion of data entries, each pair comprising information identifying a first label and a second label;
calculating a similarity score for each of the pairs comprising calculating a collaborative filtering similarity score for the first label and the second label identified by the pair using a first vector of text sequences associated with the first label and a second vector of text sequences associated with the second label, wherein a text sequence is associated with a label when the text sequence appears in a previous incoming text associated with the label; and
updating the second portion of the data entries to identify the pairs and the respective similarity scores;
processing a request for labels having similar associated text sequences;
identifying, based on the pairs and the respective similarity scores, a set of pairs having the same first label; and
selecting a pair of the identified set of pairs as having a higher respective similarity score than one or more other pairs of the identified set of pairs.

2. The system of claim 1, wherein the collaborative filtering similarity score comprises a cosine similarity score.

3. The system of claim 1, the database system further configurable to cause:
determining that the similarity score for the selected pair is higher than a designated similarity threshold; and
transmitting the first label and the second label of the selected pair to a computing device as a similar pair of labels.

4. The system of claim 3, wherein the similar pair of labels is transmitted to the computing device with one or more other pairs of labels.

5. The system of claim 1, wherein the similar pair of labels is selected in response to receiving from the computing device a request for similar pairs of labels.

6. The system of claim 1, the database system further configurable to cause:
responsive to a request to associate a label with an incoming text, updating the first portion of data entries with one or more text sequences of the incoming text and the requested label.

7. The system of claim 6, the database system further configurable to cause:
for each text sequence of the incoming text:
identifying or creating a first data entry of the first portion of data entries that includes the text sequence and the requested label; and
incrementing the text-to-label association score of the identified or created first data entry by an inflation factor.

8. The system of claim 7, wherein the inflation factor has a value based on a measure of time.

9. The system of claim 1, the database system further configurable to cause:
periodically identifying one or more incoming texts and associated labels, wherein each incoming text includes one or more text sequences; and
for each incoming text and associated label, updating the first portion of data entries with the one or more text sequences of the incoming text and the associated label.

10. The system of claim 9, wherein the incoming texts are text documents, wherein a label is a file name of a text document, and wherein the similarity scores identify text documents that are different versions of the same original text document.

11. The system of claim 1, wherein an incoming text is one of: a social media message, a text document, a customer relationship management (CRM) object including textual data, a feed item, and an article.

12. The system of claim 1, wherein a text sequence includes one or more words.

13. The system of claim 1, wherein a label is a user-generated topic assigned to a social media message or a CRM object having textual data.

14. The system of claim 1, wherein a label is a hashtag.

15. The system of claim 1, wherein the similarity score of a pair including a first label and a second label is based at least in part on a frequency of one or more text sequences appearing in a first one or more previous incoming texts associated the first label and in a second one or more previous incoming texts associated with the second label.

16. The system of claim 1, wherein the similarity score of a pair is normalized for a frequency at which the text sequences associated with the labels are used in incoming texts.

17. The system of claim 1, wherein calculating the similarity score for each of the one or more pairs comprises using a collaborative filtering method to determine cosine similarity scores for the pairs, and wherein the second portion of the data entries is a collaborative filter table having the one or more pairs recorded therein based on the analysis.

18. One or more computing devices for identifying similar labels to a user, the one or more computing devices comprising:
one or more hardware processors configurable to cause:
maintaining, by one or more servers, a plurality of data entries, each data entry of a first portion of the data entries identifying: a text sequence, a label, and a text-to-label association score indicating a number of times that the text sequence appears in one or more previous incoming texts associated with the label, and each data entry of a second portion of the data entries identifying: a first label, a second label, and a similarity score;
generating a plurality of pairs based on the first portion of data entries, each pair comprising information identifying a first label and a second label;
calculating a similarity score for each of the pairs comprising calculating a collaborative filtering similarity score for the first label and the second label identified by the pair using a first vector of text sequences associated with the first label and a second vector of text sequences associated with the second label, wherein a text sequence is associated with a label when the text sequence appears in a previous incoming text associated with the label; and
updating the second portion of the data entries to identify the pairs and the respective similarity scores;
processing a request for labels having similar associated text sequences;
identifying, based on the pairs and the respective similarity scores, a set of pairs having the same first label; and
selecting a pair of the identified set of pairs as having a higher respective similarity score than one or more other pairs of the identified set of pairs.

19. A non-transitory computer-readable storage medium storing instructions executable by a computing device for identifying similar labels to a user, the instructions being configurable to cause:
maintaining, through one or more databases, a plurality of data entries, each data entry of a first portion of the data entries identifying: a text sequence, a label, and a text-to-label association score indicating a number of times that the text sequence appears in one or more previous incoming texts associated with the label, and each data entry of a second portion of the data entries identifying: a first label, a second label, and a similarity score;
generating a plurality of pairs based on the first portion of data entries, each pair comprising information identifying a first label and a second label;
calculating a similarity score for each of the pairs comprising calculating a collaborative filtering similarity score for the first label and the second label identified by the pair using a first vector of text sequences associated with the first label and a second vector of text sequences associated with the second label, wherein a text sequence is associated with a label when the text sequence appears in a previous incoming text associated with the label; and
updating the second portion of the data entries to identify the pairs and the respective similarity scores;
processing a request for labels having similar associated text sequences;
identifying, based on the pairs and the respective similarity scores, a set of pairs having the same first label; and
selecting a pair of the identified set of pairs as having a higher respective similarity score than one or more other pairs of the identified set of pairs.