Device, Method and User Interface for Displaying Relationships between Different Sets of Data

Abstract

A device with one or more processors and memory display, in a data display panel, a plurality of nodes associated with a data set. The plurality of nodes include a first set of one or more nodes and a second set of one or more nodes distinct from the first set of nodes. The data set includes information indicating a magnitude of a relationship between nodes in the first set of nodes and nodes in the second set of nodes. The device receives a first input corresponding to selection of a first node in the first set of nodes. In response to receiving the input, the device displays, in the data display panel, a visual indication of a magnitude of a relationship between the first node and one or more nodes in the second set of nodes.

Description

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent Application No. 61/734,927, filed Dec. 7, 2012, entitled “Systems and Methods for Presenting Analytic Data” which is incorporated herein by reference in its entirety.

This application is related to the following applications: U.S. patent application Ser. No. ______, “Device, Method and User Interface for Presenting Analytic Data,” filed ______, (Attorney Docket No. 070407-5001US); U.S. patent application Ser. No. ______, “Device, Method and User Interface for Determining a Correlation between a Received Sequence of Numbers and Data that Corresponds to Metrics,” filed ______, (Attorney Docket No. 070407-5009US); U.S. patent application Ser. No. ______, “Device, Method and User Interface for Switching between Graphical Representations of Data,” filed ______, (Attorney Docket No. 070407-5011US); and U.S. patent application Ser. No. ______, “Device, Method and User Interface for Emphasizing Divisions in Data,” filed ______, (Attorney Docket No. 070407-5012US), which are incorporated by reference herein in their entirety.

TECHNICAL FIELD

The disclosed implementations relate generally to methods of data visualization.

BACKGROUND

An online community is a website designed for users to interact with each other, usually with some common theme. Unlike a traditional website, in which the website owner controls all of the content, an online community enables and encourages users to participate in the content. Users post comments, replies to comments, questions, and answers to other users' questions; more experienced users develop articles and knowledge bases, and lead forum discussions or blogs.

Business entities now recognize the value of having an online community for the business. In this case, the community focus is on the products or services of the business, and users participate in the community just like any other online community. While online communities can be beneficial for marketing, online communities are not just a marketing gimmick. For example, real users post real questions, and the questions are frequently answered by other users in the community. Of course an online community is much more than just a question and answer board.

SUMMARY

It is import to measure the success and “health” of an online community. To make these measurements, an abundance of data is tracked about user interactions with the community. Every interaction is tracked, as well as information about the interaction, such as where it originated, what time, what type of computing device the user was using, the content of the interaction itself (such as a post), as well as subsequent responses to the interaction, such as other users designating the comment or answer as useful. This abundance of data is almost too much, and previous methods of reviewing the data have been cumbersome or ineffective. Because of the shortcoming of previous attempts, implementations of the present invention provide simpler and more effective ways of reviewing interaction data for an online community.

In some implementations, a method includes, at a computing device with a display, displaying, in a data display panel, a plurality of nodes associated with a data set, where the plurality of nodes include a first set of one or more nodes and a second set of one or more nodes distinct from the first set of nodes, and the data set includes information indicating a magnitude of a relationship between nodes in the first set of nodes and nodes in the second set of nodes. The method further includes receiving a first input corresponding to selection of a first node in the first set of nodes; and in response to receiving the input, displaying, in the data display panel, a visual indication of a magnitude of a relationship between the first node and one or more nodes in the second set of nodes.

In accordance with some embodiments, a computer system (e.g., a search client system or search server system) includes one or more processors, memory, and one or more programs; the one or more programs are stored in the memory and configured to be executed by the one or more processors and the one or more programs include instructions for performing the operations of the method described above. In accordance with some embodiments, a non-transitory computer readable storage medium has stored therein instructions which when executed by one or more processors, cause a computer system (e.g., a search client system or search server system) to perform the operations of the methods described above.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a conceptual block diagram of an online community in accordance with some implementations.

FIG. 2 is a block diagram of a client device in accordance with some implementations.

FIG. 3 is a block diagram illustrating the structure of web servers, database servers, and application servers in accordance with some implementations.

FIGS. 4A-4D are flow diagrams illustrating a method of displaying a magnitude of a relationship between nodes corresponding to a data set in a data viewing interface in accordance with some implementations.

FIGS. 5A-5DD illustrate methods of presenting, selecting, and changing views of analytic data in accordance with some implementations.

FIG. 6 illustrates an example of a data flow and architecture for collection of analytics data for an online community and presentation of analytic data for that online community in accordance with some implementations.

Like reference numerals refer to corresponding parts throughout the several views of the drawings.

DESCRIPTION OF IMPLEMENTATIONS

Reference will now be made in detail to implementations, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the implementations.

FIG. 1 illustrates an example of an environment in which disclosed implementations operate. In the middle is one or more communication networks 112, such as the Internet, that link together the various computing devices. Users 102 are individual people who access the communication networks 112 using electronic devices 104, such as desktop computers, laptop computers, tablet computers, smartphones, PDA's, etc. The communication networks are also connected to one or more Social Media sites 106, such as Facebook, Youtube, etc.

Also connected to the communication network 112 is an online community 100 for a business entity. An online community 100 provides public forums, bulletin boards, blogs, and other information. Unlike a traditional business website, an online community is maintained by everyone, including the users 102. Users 102, for example, can post questions about products or services provided by the business entity, or can post answers to other users questions. An online community can increase profitability of the business entity in many ways, including reducing the costs for customer support (users find the information in the community) and reducing the cost of search engine optimization (for example, because search engines review the user generated community content).

An online community 100 includes one or more web servers 114 that handle the incoming web requests, and one or more application server 116 that provide the user interface functionality for the community. The online community 100 also includes one or more database servers 118 that store the data for the community, including logs of user interactions. In some implementations, the database servers 118 also store binary files, image files, video files, and so on. In some implementations, binary, image, and video files are stored on the application servers 116, of other file servers (not shown).

In addition to the users 102 who interact directly with the online community 100, managers, stakeholders, and executives 108 of the business entity review the community interaction data. As described in more detail below, some people (e.g., managers, stakeholders and/or executives) 108 review the raw data, and other people 108 review analytic computed data. The people 108 utilize client devices 110 to access the interaction data over the communication network 112. Like a user device, a client devices can be desktop computers, laptop computers, tablet computers, smartphones, PDA's, etc.

Although FIG. 1 illustrates Web Servers 114, application servers 116, and database servers 118 as physically distinct devices, the functionality can be combined in various different ways. For example, the web server software and application server software optionally runs on the same server(s).

FIG. 2 illustrates an example of a client device 110. A client device 110 generally includes one or more processing units (CPUs) 202, one or more network or other communications interfaces 204, memory 214, and one or more communication buses 212 for interconnecting these components. The communication buses 212 optionally include circuitry (sometimes called a chipset) that interconnects and controls communications between system components. A client device 110 also includes a user interface 206, for instance a display 208 and a keyboard or other input mechanism 210. Some client devices 110 display a keyboard 210 on the display 208 as needed. Some client devices 110 utilize a touchscreen or touch-sensitive display 208. Memory 214 optionally includes high-speed random access memory, such as DRAM, SRAM, DDR RAM or other random access solid state memory devices; and optionally includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices. Memory 214 optionally includes mass storage that is remotely located from the central processing unit(s) 202. Memory 214, or alternately the non-volatile memory device(s) within memory 214, comprises a computer readable storage medium. In some implementations, memory 214 or the computer readable storage medium of memory 214 stores the following programs, modules and data structures, or a subset thereof:

• • an operating system 216 (e.g., Windows, Mac OS X, iOS, Android, or Linux) that generally includes procedures for handling various basic system services and for performing hardware dependent tasks;
  • a network communications module 218 that is used for connecting the client device 110 to servers or other computing devices via one or more communication networks, such as the Internet, other wide area networks, local area networks, metropolitan area networks, and the like;
  • a web browser 220 (e.g., Internet Explorer, Safari, Chrome) that is used to access web pages, web applications, and other resources over a communication network, such as the Internet, other wide area networks, local area networks, metropolitan area networks, and the like;
  • a metric explorer interface 5000, enabling a user to graphically view data from user interaction with an online community or other social media, and to select many different aspects (or dimensions) of what data is viewed, and how the data is viewed. The metric explorer interface is described in more detail with reference to FIGS. 9-005 to 9-141 of U.S. Provisional Patent Application No. 61/734,927, filed Dec. 7, 2012, entitled “Systems and Methods for Presenting Analytic Data”;
  • an actionable analytics interface 222, enabling a user to graphically review calculated analytic data based on users' interactions with an online community or other social media. The actionable analytics interface 222 is described in more detail with reference to FIGS. 5A-5DD below and FIGS. 9-142 to 9-206 of U.S. Provisional Patent Application No. 61/734,927, filed Dec. 7, 2012, entitled “Systems and Methods for Presenting Analytic Data”;
  • a return on investment (ROI) calculation interface 224, enabling a user to compute the return on investment for an online community 100, or compute correlations between business key performance indicators (KPIs) and various metrics. The ROI calculation interface 224 is described in greater detail with reference to FIGS. 9-207 to 9-236 of U.S. Provisional Patent Application No. 61/734,927, filed Dec. 7, 2012, entitled “Systems and Methods for Presenting Analytic Data”; and
  • one or more browser cookies 226, which save state or other information for a web page or web application.

FIG. 3 illustrates an example of a server, such as a web server 114, an application server 116, or a database server 118. A server 114/116/118 generally includes one or more processing units (CPUs) 302, one or more network or other communications interfaces 304, memory 314, and one or more communication buses 312 for interconnecting these components. The communication buses 312 optionally include circuitry (sometimes called a chipset) that interconnects and controls communications between system components. The server 114/116/118 optionally includes a user interface 306, for instance, a display 308 and a keyboard or other input device 310. Memory 314 optionally includes high-speed random access memory, such as DRAM, SRAM, DDR RAM or other random access solid state memory devices; and optionally includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices. Memory 314 optionally includes mass storage that is remotely located from the central processing unit(s) 302. Memory 314, or alternately the non-volatile memory device(s) within memory 314, comprises a computer readable storage medium. In some implementations, memory 314 or the computer readable storage medium of memory 314 stores the following programs, modules and data structures, or a subset thereof:

• • an operating system 316 (e.g., Linux or Unix) that generally includes procedures for handling various basic system services and for performing hardware dependent tasks;
  • a network communications module 318 that is used for connecting the server 114/116/118 to other servers or other computing devices via one or more communication networks, such as the Internet, other wide area networks, local area networks, metropolitan area networks, and the like;
  • web server software 320 (e.g. Apache web server or Apache Tomcat) that receives web requests, and delivers appropriate web pages or other resources in response to the requests;
  • database server software 322 (e.g., MySQL or other structured query language (SQL) database engine), which stores organized relational data for the online community 100, and provides the data as needed;
  • a database 324 that contains one or more community interaction logs. The log database(s) track information about user interactions with the online community. For example, clicking to view a web page generates a “page view” event, which is logged. The log record for this event optionally includes the user name or ID, the time of the page view, which page was viewed, etc.;
  • a forum database 326, which includes data for one or more community forums. This includes all data and metadata for posts to each forum, as well as configuration information about each forum;
  • a blog database 328, which stores blogs for one or more individuals, including all of the content of the blog, as well as comments, etc.;
  • a tribal knowledge base database 330, which is an organized collection of articles that have generally proceeded through a review process, providing useful information on a topic (e.g., how to utilize a feature of a product provided by the business entity);
  • an application server 332, which provides the functionality of the online community 100 and functionality to access the interaction data 324 generated by users 102 of the online community 100;
  • one or more community interaction applications or interfaces 334, which are used by users 102 who participate in the online community 100;
  • a metric explorer interface 5000, which is used by users (e.g., Managers) to review the raw interaction data. This is described in more detail with reference to FIGS. 9-005 to 9-141 of U.S. Provisional Patent Application No. 61/734,927, filed Dec. 7, 2012, entitled “Systems and Methods for Presenting Analytic Data”;
  • an actionable analytics interface 222, which is used by users (e.g., Stakeholders) to review computed analytic data based on the interactions of users 102 with the online community 100. The actionable analytics interface 222 is described in more detail with reference to FIGS. 5A-5DD below and FIGS. 9-142 to 9-206 of U.S. Provisional Patent Application No. 61/734,927, filed Dec. 7, 2012, entitled “Systems and Methods for Presenting Analytic Data”;
  • a KPI/ROI calculation interface 224, which is used by users (e.g., Executives) to evaluate the return on investment for an online community. In some implementations, the interface 224 computes correlations between business key performance indicators and community metrics. This is described in greater detail with reference to FIGS. 9-207 to 9-236 of U.S. Provisional Patent Application No. 61/734,927, filed Dec. 7, 2012, entitled “Systems and Methods for Presenting Analytic Data”;
  • a KPI calculation engine 336, which computes correlation coefficients between sets of data;
  • a data export application 338, which converts retrieved data into various external file formats, including CSV (comma separated values), PDF (portable document format), PNG (portable network graphics), and XLS (Microsoft Excel).

Although FIG. 3 shows examples of servers, it is intended more as functional description of the various features which are, in some circumstances, present in a set of servers than as a structural schematic of the implementations described herein. In practice, and as recognized by those of ordinary skill in the art, items shown separately could be combined and some items could be separated. For example, some items shown separately in FIGS. 1 and 3 could be implemented on single servers and single items could be implemented by one or more servers. The actual number of servers used to implement the web servers, application servers, and database servers, and how features are allocated among them will vary from one implementation to another, and optionally depends in part on the amount of data traffic that the system must handle during peak usage periods as well as during average usage periods.

Each of the methods described herein are, optionally, performed by instructions that are stored in a computer readable storage medium and that are executed by one or more processors of one or more servers or clients. Each of the operations shown in FIGS. 4A-4D optionally correspond to instructions stored in a computer memory or computer readable storage medium.

Methods for Displaying Data

FIGS. 4A-4D are flow diagrams illustrating a method 400 of displaying a magnitude of a relationship between nodes corresponding to a data set in a data viewing interface in accordance with some implementations. The method 400 is performed at a computing device (e.g., Client Device 110 in FIG. 2, Web Server 114 in FIG. 3, Application Server 116 in FIG. 3, or Database Server 118 in FIG. 3) with a display and an input device (e.g., a mouse, a trackpad, a touch screen or voice control module). Some operations in method 400 are, optionally, combined and/or the order of some operations is, optionally, changed. As described below, the method 400 provides an intuitive way to display a magnitude of a relationship between nodes corresponding to a data set in a data viewing interface. The method reduces the cognitive burden on a user when viewing data, thereby creating a more efficient human-machine interface. For battery-operated electronic devices, enabling a user to view data faster and more efficiently conserves power and increases the time between battery charges.

The device displays (402), in a data display panel, a plurality of nodes associated with a data set, (e.g., as shown in FIGS. 5B-5DD). The plurality of nodes include (404) a first set of one or more nodes and a second set of one or more nodes distinct from the first set of nodes. In some implementations, the first set of nodes correspond (406) to a first set of divisions in a first data-set dimension. In some implementations, the second set of nodes correspond (408) to a second set of divisions in a second data-set dimension different from the first data-set dimension.

The data set includes (410) information indicating a magnitude of a relationship between nodes in the first set of nodes and nodes in the second set of nodes. In some implementations, a magnitude of a relationship between a first node and a second node is based on a number of data points that are associated with both the first node and the second node relative to a total number of data points in the data set. In some implementations, a magnitude of a relationship between a first node and a second node is based on a number of data points that are associated with both the first node and the second node relative to a total number of data points associated with the first node. For example, if the first node is a respective source of internet traffic (e.g., a search engine such as Google.com) and the second node is a respective section of a website (e.g., user forums) that receives internet traffic, the magnitude of the relationship between the respective source and the respective section of the website is an amount of internet traffic (e.g., a number of requests/communications or a quantity of data transferred) that the respective section of the website receives from the respective source (e.g., as determined based on referrer information in the internet traffic).

After displaying the plurality of nodes in the data display panel, the device receives (412) a first input (e.g., from the input device) corresponding to selection of a first node in the first set of nodes (e.g., as shown in FIG. 5B). In response (414) to receiving the input, the device displays (416), in the data display panel, a visual indication of a magnitude of a relationship between the first node and one or more nodes in the second set of nodes (e.g., as shown in FIG. 5C). In some implementations, the visual indication of the magnitude of the relationship between the first node and one or more nodes in the second set of nodes includes (418) a connector between the first node and the one or more nodes in the second set of nodes. In some implementations, a first connector width of the connector (e.g., a line width of the connector when the connector is a line) proximate to the first node corresponds to an aggregate magnitude of the relationship between the first node and the one or more nodes (e.g., the nodes for which the relationship with the first node is currently displayed) in the second set of nodes. In some implementations, a second connector width of the connector proximate to a respective node in the second set of nodes corresponds to a magnitude of the relationship between the first node and the respective node (e.g., as shown in FIGS. 5B-5R).

In some implementations, the visual indication includes a number providing a relative or absolute value corresponding to the magnitude of the relationship between the first node and the one or more nodes in the second set of nodes. In some implementations, the visual indication includes a number providing a relative or absolute value corresponding to the magnitude of the relationship between the first node and the respective node. In some implementations, the first connector width is the same as the second connector width. In some implementations, the first connector width is different from the second connector width. In some implementations, the one or more nodes in the second set of nodes includes a plurality of nodes.

In some implementations, the relationship between the first node and the one or more second nodes is (424) a flow of network traffic. In some implementations, the first node is a source of network traffic and the one or more second nodes are destinations of network traffic. In some implementations, the first node and the one or more second nodes are represented by words (or other symbols) visually identifying the sources and destinations of network traffic.

In some implementations, in response to receiving the first input, the device displays (426) a visual indication of the magnitude of the relationship between the first node and a plurality of nodes in the second set of nodes (e.g., as shown in FIGS. 5C-5H). In some implementations, in response to receiving the first input, the device displays (428) a visual indication of the magnitude of the relationship between the first node and each node in the second set of nodes (e.g., as shown in FIGS. 5C-5H).

In some implementations, after displaying the visual indication of the magnitude of the relationship between the first node and one or more nodes in the second set of nodes, the device receives (430) a second input (e.g., from the input device) corresponding to selection of a second node the second set of nodes for which a respective visual indication of a magnitude of a relationship between the first node and the second node is displayed. In some implementations, in response to receiving the second input, the device ceases (432) to display the respective visual indication of the magnitude of the relationship between the first node and the second node (e.g., as shown in FIGS. 5H-5I).

In some implementations, after displaying the visual indication of the magnitude of the relationship between the first node and one or more nodes in the second set of nodes, while the first node is selected, the device receives (434) a third input (e.g., from the input device) corresponding to selection of a second node the second set of nodes for which a respective visual indication of a magnitude of a relationship between the first node and the second node is not displayed. In some implementations, in response to receiving the third input, the device displays (436) a respective visual indication of the magnitude of the relationship between the first node and the second node (e.g., as shown in FIGS. 5I-5J).

In some implementations, after displaying the visual indication of the magnitude of the relationship between the first node and one or more nodes in the second set of nodes, the device receives (438) a fourth input (e.g., from the input device) corresponding to a request to display sub-nodes of a second node the second set of nodes for which a respective visual indication of a magnitude of a relationship between the first node and the second node is displayed. In some implementations, in response (440) to receiving the fourth input, the device displays (442) a plurality of sub-nodes of the second node, including a first sub-node and a second sub-node, the device displays (444) a visual indication that indicates a magnitude of relationship between the first node and the first-sub node of the second node, and the device displays (446) a visual indication that indicates a magnitude of relationship between the first node and the second-sub node of the second node (e.g., as shown in FIGS. 5Q-5R and FIG. 5DD).

In some implementations, the visual indication of the magnitude of the relationship between the first node and one or more nodes in the second set of nodes includes (448) a visual indication of a magnitude of a relationship between the first node and a second node the second set of nodes. In some implementations, after displaying the visual indication of the magnitude of the relationship between the first node and one or more nodes in the second set of nodes, the device receives (450) a fifth input (e.g., from the input device) corresponding to a request to display sub-nodes of the first node. In some implementations, in response (452) to receiving the fifth input, the device displays (454) a plurality of sub-nodes of the first node, including a first sub-node and a second sub-node.

In some implementations, the first node includes (456) more sub-nodes than can be displayed in the data display panel (e.g., there are more sub-nodes that correspond to the first node than there are slots in the data display panel to display representations of the sub-nodes), and displaying the plurality of sub-nodes of the first node includes displaying a subset of (e.g., less than all of) the sub-nodes associated with the first node (e.g., as shown in FIGS. 5W-5DD). In some implementations, the subset of the sub-nodes associated with the first node is selected (458) automatically (e.g., selected by the device without user intervention) based on predefined criteria. For example, the device selects the most popular nodes across multiple data sets or selects the nodes with the highest magnitude relationship with the second node. In some implementations, the subset of the sub-nodes associated with the first node is selected (460) based on user-provided criteria. For example, the device selects sub-nodes that match a user specified search query such as “com,” as shown in FIG. 5AA.

In some implementations in response to receiving the fifth input, the device displays (462) a visual indication that indicates a magnitude of relationship between the first-sub node of the first node and the second, and displays (464) a visual indication that indicates a magnitude of relationship between the second-sub node of the first node and the second node (e.g., as shown in FIGS. 5M-5Q).

It should be understood that the particular order in which the operations in FIGS. 4A-4D have been described is merely an example and is not intended to indicate that the described order is the only order in which the operations could be performed. One of ordinary skill in the art would recognize various ways to reorder the operations described herein. Additionally, it should be noted that details of other processes described in U.S. Provisional Patent Application No. 61/734,927, filed Dec. 7, 2012, entitled “Systems and Methods for Presenting Analytic Data” with respect to other methods described therein (e.g., methods 400, 500, 600 and 800 in U.S. Provisional Patent Application No. 61/734,927) are also applicable in an analogous manner to method 400 described above with respect to FIGS. 4A-4D. For example, the inputs, data display panels, data selection interfaces, and data-set dimensions described above with reference to method 400 optionally have one or more of the characteristics of the inputs, data display panels, data selection interfaces, and data-set dimensions described in U.S. Provisional Patent Application No. 61/734,927, filed Dec. 7, 2012, entitled “Systems and Methods for Presenting Analytic Data” with reference to other methods described therein (e.g., methods 400, 500, 600 and 800 in U.S. Provisional Patent Application No. 61/734,927). For brevity, these details are not repeated here.

Participation in an online community or other social media generates a lot of data. For example, when a user posts a question, comment, or answer, the post itself is recorded, as well as the date/time of the post, who made the post, etc. Different people related to the community or social media are interested in different aspects of the data. In some implementations, the parties who review the data (e.g., users of the device described above) are categorized into three roles, including Managers, Stakeholders, and Executives.

A manager is someone who participates in the day-to-day management of an online community platform. Manager reviews the direct measurements of participation by community members. For example, a manager would review raw metrics such as the total number of posts, the total number of posts of specific types (e.g., questions, answers), the number of fans, the number of discussion threads and the lengths of those threads, the number of times particular content is viewed by users, the comments or ratings received by each particular user and/or posts, and so on. These quantities are directly measurable by clicks, button presses, and other recorded user interactions, and constitute level 1 data. In general, the level 1 data reviewed by managers comprises raw metrics and simple reports based on the raw metrics.

A business stakeholder is someone who derives business value from the online community. Business stakeholders are less interested in the raw metrics collected about a community, and are more interested in data that shows the success or effectiveness of an online community or other social media. For example, a stakeholder reviews market share or share of the advertising in the market, or other data that demonstrates how well the media content resonates with the participants of the community. The data of interest to the business stakeholders are actionable analytics generated from the raw metrics. Various statistical and analytical techniques can be used to generate the analytics from the raw metrics. Frequently the actionable analytics have complex and non-linear relationships with the raw metrics from which the analytics are derived. The level 2 data reviewed by business stakeholders comprises actionable analytics.

Finally, business executives review data that ties social media directly to business objectives. Generally, the business objectives are financial, such as revenue or profit, but also include less tangible objectives such as customer satisfaction. The level 3 data reviewed by business executives comprise key performance indicators (KPI's), return on investment (ROI), and so on. The level 3 data is derived from the raw metrics and the actionable analytics associated with the community, as well as additional data (e.g., returns, revenues, and customer satisfaction metrics) provided by the entity using the community to promote its business objectives.

FIGS. 4A-4DD below describe a user interface displayed on a display that is coupled to a computing device with one or more processors and memory that store programs that are configured to be executed by the one or more processors. Below, when a user is described as performing an operation associated with a displayed user interface (e.g., selecting an option, activating an affordance, or the like), the computer is detecting an input provided by the user (e.g., using a user input device such as a mouse, a touchpad, a keyboard, voice commands, etc.), and the computing device is responding to the detected input by updating the user interface and/or performing an associated operation in accordance with the detected input. Thus, for example, when a user “selects a time range to be displayed,” the device is detecting an input generated by a user with an input device (e.g., a mouse, trackpad or keyboard) that corresponds to selecting the time range, and the device is responding to the input that corresponds to selecting the time range by selecting the time range.

FIG. 5A illustrates some drawbacks of previous techniques for displaying bipartite data. In these figures, the data represents “referrer” traffic. That is, for users reaching a certain website, where were they referred from. FIG. 5A presents the referrer traffic data as a bipartite graph 5372, with lines connecting each referrer in the first column 5374 to the corresponding referred sites in the second column 5376. The jumble of lines is also not easy to understand.

FIGS. 5B-5R illustrate converting a bipartite graph into a more usable data visualization according to some implementations. In some implementations, the referrers in the first column 5378 are grouped together into logical groupings, such as “Search Engines,” “Social Networks,” and “Others” Similarly, in some implementations, the destination websites for which referral traffic is measured are grouped in the second column 5380. For example, the groups of destination websites include “Forum”, “Blogs”, “Ideas”, “TKB”, and “Events.”

With this grouped bipartite structure, a user can select a single group node in the referrer column (e.g., a first set of nodes), such as by selecting node 5382 in FIG. 5B to see the amount of traffic referred from that node. This is illustrated in FIG. 5C. In FIG. 5C, the “Search Engine” node 5382 had 30,267 referrals 5384 in the selected reporting period. These referrals from the “Search Engine” node are split among the five destination groups in the destination column (i.e., a second set of nodes). The destination groups in the destination column are represented by the “Forums” node 5392, the “Blogs” node, the “Ideas” node, the “TKB” node, and the “Events” node. The forums group 5392 received 8232 referrals 5390 from the search engine group 5382. As illustrated in FIG. 5C, there is a single line or edge 5386 emanating from the “Search Engine” referral group 5382, whose size is proportional to the number of referrals (i.e., 30267 in this illustration). Somewhere between the referrer column 5378 and the destination column 5380, the single curve 5386 splits into five curves, each leading to one of the destination groups. The sizes (e.g., thickness) of these curves are similarly proportional to the number of referrals from the “Search Engine” group to each of the destination group in the destination column. In this example, the curve 5388 going to the Forums group 5392 has the second largest width because it has the second largest amount of referral traffic (i.e., 8232 referrals, as shown in count 5390). As shown in FIG. 5C the total counts add up—the total 30,267 equals the sum of the counts to the destinations (i.e., referred websites) (8232+12732+5132+1039+3132=30267). FIG. 5D is similar to FIG. 5C, illustrating the referral traffic from the “Social Networks” group. In some implementations, a user can switch to viewing the data from a desired node by clicking on the desired node. FIGS. 5E-5G illustrate that the same functionality can be applied on the nodes in the destination column. In these figures, a user has selected a destination group in the destination column, and the graph shows all of the referrals that have led to that destination group from each of the referral groups.

In some implementations, a data display panel is used to present the relationship data between a first group of nodes and a second group of nodes. As illustrated in FIGS. 5B through FIG. 5H, a first set of nodes are presented in a first column (e.g., the referrer column), and a second set of nodes are presented in a second column (e.g., the destination column) parallel to the first column. In some implementations, the first set of nodes represent divisions in a first data set dimension (e.g., different groups of referrers), and the second set of nodes represent divisions in a second data set dimension (e.g., different groups of destinations for the referral traffic). Each node in the first set is related to at least one of the nodes in the second set by some type of relationship (e.g., relationship between a referrer and a destination of the referral). A magnitude of a relationship between a first node in the first column and a second node in the second column is based on a number of data points that are associated with both the first node and the second node. In a particular example, a magnitude of a relationship between a referrer node “Search Engine” and a destination node “Blogs” is the amount of referral traffic going from the referrer node “Search Engine” to the destination node “Blogs”. In this example, as shown in FIG. 5C, the magnitude of relationship is 12,732 referrals. Similarly, in this particular example, a magnitude of a relationship between a destination node “Forums” and a referrer node “Social Networks” is the amount of referral traffic going from the referrer node “Social Network” to the destination node “Forums”. In this example, as shown in FIG. 5E, the magnitude of relationship is 3201 referrals.

In some implementations, a user can select one of the nodes in the first column, and in response to the selection, a visual indication of a magnitude of a relationship between the selected node in the first column and one or more nodes in the second column is presented. As shown in FIG. 5C, when a first node “Search Engine” is selected in the referrer column, visual indications (e.g., lines of various thicknesses) leading from the first node to the various nodes in the destination column are displayed. The thicknesses of the lines correspond to the amount of referral traffic going from the referrers represented by the first node to the destinations represented by the second node. In some implementations, the actual numbers of the referrer traffic leaving the first node is presented near the first node and the thickness of the line originating from the first node corresponds to the amount of referrer traffic leaving the first node. In addition, the actual numbers of the referrer traffic arriving at each of the nodes in the second column are presented near each of the nodes in the second column. The thickness of the line originating from the first node is split into several lines each leading to a respective node in the second column. The thicknesses of the lines leading to each of the nodes in the second column correspond to the amount of referrer traffic arriving at the node. As shown in FIGS. 5C and 5D, when a different referrer node is selected in the first column, a different set of lines are presented to represent the magnitude of relationships between the newly selected node in the first column and the various nodes in the second column.

In some implementations, a user can select one of the nodes in the second column, and in response to the selection, a visual indication of a magnitude of a relationship between the selected node in the second column and one or more nodes in the first column is presented. As shown in FIG. 5E, when a node “Forums” is selected in the destination column, visual indications (e.g., lines of various thicknesses) linking the selected node to the various nodes in the referrer column are displayed. The thicknesses of the lines correspond to the amount of referral traffic going from the referrers represented by the various nodes in the referrer column to the destinations represented by the selected node. In some implementations, the actual numbers of the referrer traffic leaving the each node in the referrer column is presented near the node and the thickness of the line originating from the node corresponds to the amount of referrer traffic leaving the node. In addition, the actual numbers of the referrer traffic arriving at the selected node in the destination column are presented near the selected node in the destination column. The thicknesses of the lines originating from the various nodes in the referrer column are merged into a single thicker line leading to the selected node in the destination column. The thicknesses of the lines leading to the selected node in the destination column correspond to the amount of referrer traffic arriving at the selected node. As shown in FIGS. 5E and 5G, when a different destination node is selected in the second column, a different set of lines are presented to represent the magnitude of relationships between the newly selected node in the second column and the various nodes in the first column.

FIGS. 5H-5M illustrate that a user can limit the referral graph on both ends at the same time. In some implementations, holding down the Shift or Ctrl key on the keyboard while selecting another node modifies the selected graph so that the previously selected node is still selected, as well as the newly selected node. For example, in FIG. 5H, a user has selected the Search Engine node 5382, and thus the graph shows the traffic to each of the five destination groups in the second column. Subsequently, in FIG. 5I, a user has additionally selected the “Blogs” group 5394 in the second column. Having selected just the Search Engine node 5382 in the first column and the Blogs node 5394 in the second column, the graph shows just the traffic between these two nodes. The lines indicating the magnitudes of relationships between the “Search Engine” node and the other nodes in the second column have been removed in response to the selection of the Blog node in the second column. In FIG. 5J, the user has further selected the “Ideas” node 5396 in the second column. Based on the concurrent selection of the three nodes 5382, 5394, and 5396, the interface displays the lines indicating the amount of traffic (i.e., the magnitudes of relationships in this example) going from the “Search Engine” node 5382 in the first column to the two selected destination group nodes 5394 and 5396. As shown in FIG. 5J, the left side portion of the line originating from the selected node (e.g., “Search Engine” node 5382) in the first column represents the total amount of referral traffic going from the selected node in the first column to the two selected nodes in the second column. The right portion of the line splits into two lines each leading to a respective one of the two selected nodes in the second column. The thicknesses of the two lines are representative of the respective amounts of referral traffic going to the two selected nodes in the second column.

In FIG. 5K, the user has additionally selected the “Events” website group 5400, so that the four nodes 5382, 5394, 5396, and 5400 are all selected. Based on this selection, the interface displays a graph showing the traffic from the referrer node “search engines” 5382 to the three selected destination nodes 5394, 5396, and 5400. Again, the left portion of the line have a thickness indicative of the total amount of traffic going from the selected node in the first column to the three selected nodes in the second column. The right portion of the line splits into three different lines, with respective thicknesses indicative of the respective amounts of referral traffic going to the three selected destination nodes in the second column.

In some implementations, using the same technique to select additional nodes, a user can deselect a currently selected node, which is illustrated in FIG. 5L. In this figure, a user has deselected the “Ideas” node 5396, so only the lines showing the referral traffic to the selected “Blogs” node 5394 and “Events” node 5400 remain displayed, and the line showing the traffic to the “Ideas” node is no longer displayed in the user interface. In addition, the number showing the amount of referral traffic originated from the “Search Engine” node is also modified accordingly. In FIG. 5M, a user has deselected the “Events” node 5400 as well (e.g., by clicking on the “Event” node 5400), as a result, only the “search engine” node 5382 in the first column and the “Blogs” node 5394 in the second column remain selected. As a result, the line between the “search engine” node 5382 to the deselected “Events” node 5400 is no longer displayed, and only the line between the “Search Engines” node and the “Blogs” node remains in the user interface. The thickness of the line represents the amount of referral traffic going from entities represented by the “Search Engines” node to the entities represented by the “Blogs” node.

FIGS. 5M-5R illustrate drilling down within one or more of the nodes to reveal information about the sub-nodes of the one or more nodes. In some implementations, a user expands a node group by clicking on an expand icon, such as expand icon 5402 in FIG. 5M. When a node (e.g., “Search Engines” node 5382) is currently selected and the node has one or more sub-nodes representing sub-groups of entities within the group of entities represented by the node, the expand icon 5402 can be presented proximate to that node in the user interface. After the user clicks on the expand icon 5402, the user interface provides additional detail, as shown in FIG. 5N. In some implementations, the expand icon 5402 changes its appearance to indicate that the associated node has been expanded, as shown in FIG. 5N. In this figure, expand icon 5402 is pointing downward, showing that the data has been expanded, whereas the expand icon 5404 associated with the “Social Networks” node is pointing to the right, indicating that the “Social Networks” node has not been expanded. In this example, the “Search Engine” node 5382 has expanded to identify three sub-nodes (e.g., sub-nodes representing search engines “google” 5406, “bing” 5408, and “yahoo” 5410). Whereas the interface previously provided a single curve from the “Search Engine” node 5382 to the “Blogs” node 5394, the interface now provides three curves individually from each of the sub-nodes of the expanded node 5382 in the first column to the selected node (e.g., the “Blogs” node 5394) in the second column. The thicknesses of the three curves originating from the three sub-nodes each represents the respective amount of referral traffic going from the entities represented by a respective one of the sub-nodes. The three curves eventually merge into a single curve leading to the selected node in the second column. The thickness of the merged curve represents the total amount of referral traffic going from the three sub-nodes in the first column to the selected node in the second column.

FIG. 5O illustrates that additional nodes can be selected, even when one or more nodes have been expanded. In this figure, a user has additionally selected the “Events” node 5400 in the second column (e.g., by holding down the Shift or Ctrl buttons while clicking on the “Events” node 5400), and thus the interface provides data for this newly selected node as well. In this figure, there are three curves from the three individual sub-nodes of the selected “Search Engine” node 5382, each of the three curves originates from a respective one of the sub-nodes 5406, 5408, and 5410. As shown in FIG. 5O, the three curves eventually merge and then split into two curves each going to one of the two selected nodes 5394 and 5400 in the second column. As illustrated here, the total amount of referral traffic originated from the three sub-nodes in the first column equals the total amount of referral traffic arriving at the two selected destination nodes in the second column (10457+3010+2307=15864=12732+3132). The thickness of each curves that ends at a respective node or sub-node are indicative of the amount of referral traffic originated or received at the entities represented by the respective node or sub-node.

FIGS. 5P and 5Q illustrate that currently selected sub-nodes, such as the sub-nodes representing individual search engines in the first column, can be deselected (e.g., by clicking on a currently selected sub-node). In FIG. 5P, the sub-node representing the “yahoo” search engine 5410 has been deselected by a user input (e.g., a click) received while the sub-node was selected and an indication of a magnitude of relationship with another node in the second column was shown in the user interface. In FIG. 5Q the sub-node representing the “bing” search engine 5408 has been deselected as well by an additional user input (e.g., a click on the currently selected sub-node).

FIG. 5R illustrates that a currently selected destination node in the second column can be expanded in the same way as a currently selected referrer node in the first column. In this example, an expand icon 5412 has been selected for the “Blogs” node 5394, so the “Blogs” node expands to display all of its sub-nodes 5414 (e.g., sub-nodes representing the individual blogs 5414 under the “Blogs” node). When a currently destination node is expanded and its sub-nodes displayed in the user interface, the curves linking the selected nodes in the first column to the selected nodes in the second column are modified as well. For example, the curve that previously represented the amount of referral traffic going to the selected “Blogs” node 5394 is now split into four separate curves, each leading to a respective one of the sub-nodes 5414. The thicknesses of the four separate curves are indicative of the respective amounts of referral traffic arriving at the four sub-nodes.

FIGS. 5M-5R illustrate drilling down a single level in a node hierarchy. In these illustrations, there are only two levels, but in other implementations, there are three or more levels. In some implementations, the same techniques are applied to expand nodes to additional sub-levels. In some implementations, a respective expand icon is presented proximate to all nodes that have sub-nodes (such as icons 5402 and 5412). When an expand icon associated with a particular is selected, the node expands to reveal all of its sub-nodes. In addition, a respective expand icon can be presented proximate to each revealed sub-node that has its own set of sub-nodes. When the expand icon associated with a revealed sub-node is selected, the set of sub-nodes of the revealed sub-node are presented. Meanwhile, the curves linking the selected nodes/sub-nodes in the first column and the selected nodes/sub-nodes in the second column are modified (e.g., split, merged, and/or made thicker or thinner) to reflect the magnitudes of relationships between the currently selected nodes/sub-nodes in the two columns.

FIGS. 5S-5DD further illustrate implementations that graphically display Internet referrer traffic. The internet referrer traffic is used as an illustrative example of the kind of hierarchical and relational data that can be selectively visualized using the bipartite user interfaces described in this specification. The hierarchical aspect of the data set means that at least one of the data-set dimensions is hierarchical, and the data can be divided into a hierarchy of groups (represented as nodes and sub-nodes in a respective column in the user interface). The relational aspect of the data set means that the data is associated with at least one group (e.g., represented as a sub-node and the sub-node's parent node(s)) in a first data-set dimension (e.g., represented by a first column in the user interface), and at least another group (represented as a sub-node and the sub-node's parent node(s)) in a second data-set dimension (e.g., represented by a second column in the user interface).

As shown in FIG. 5S, the user interface shows how nodes can be expanded and filtered in a first dimension (e.g., the referrer dimension). On the referrer side (e.g., a first column), an “All traffic” node 5416 is presented initially, with an expand icon 5418 displayed proximate to the “All traffic” node 5416. Initially, the icon 5418 is unexpanded. In this example, expanding the “all traffic” node 5416 (e.g., by selecting the expand icon 5418) presents the three sub-nodes “Search Engine” 5382, “Social Networks” 5404, and “Others” 5420 in the first column as shown in FIG. 5T. Each of these sub-nodes 5382, 5404, and 5420 has its own set of sub-nodes, as indicated by the expand icons 5402, 5422, and 5426. Expanding the “Search Engine” node 5382 reveals the sub-nodes “google” 5406, “bing” 5408, and “yahoo” 5410, as illustrated previously in FIG. 5U. There are no expand icons adjacent to the “google”, “bing”, and “yahoo” sub-nodes, because, in this example, the data set does not have further divisions beyond the groups represented by these sub-nodes. As a result, these sub-nodes cannot be expanded further in this example. In FIG. 5V, a user has collapsed the “search engine” node 5382 (e.g., by clicking on the expand icon 5402), and the node's previously revealed sub-nodes are concealed again. The user has also expanded the “Social Networks” node 5404 (e.g., using expand icon 5422). In this example, the “Social Networks” node 5404 has four sub-nodes 5424 which have now been revealed. None of the sub-nodes 5424 has any further sub-nodes.

In FIG. 5W, a user has further expanded the “Others” node 5428, for example by clicking on the expand icon 5426. The list of sub-nodes 5428 for node 5428 is too long to fit on the display at one time, so the list is truncated in this example. In FIG. 5X, a user has collapsed the “Social Networks” node 5404, providing more space for the sub-node list 5428 for the “Others” node 5420. Even with more space, the list 5428 is still too big.

FIG. 5Y illustrates that, in some implementations, a filter capability for node list is provided. A dialog box is, optionally, invoked in any of a variety of different ways, including menu commands, toolbar icons, or popup menus (e.g., right clicking on the “others” label in the display). In some implementations, the interface displays a filter dialog box 5430 automatically whenever the number of sub-nodes exceeds a specific threshold (e.g., 10). The filter dialog box 5430 enables a user to limit the set of sub-nodes presented in the list 5428.

In FIG. 5Z, a user has typed the filter string “g” 5432A into the filter dialog box 5430, and thus the filtered list 5428A includes only those nodes whose names or identifiers include the letter “g”. In FIG. 5AA, a user has entered the filter string “com” 5432B in the filter dialog box 5430, so the corresponding filtered list 5428B includes only referral nodes with “com” in the name or URL. In FIG. 5BB, a user has typed an additional “m” for the filter to create the filter string “comm” 5432C in the dialog box 5430. This leads to an even smaller filtered list 5428C of sub-nodes. In some implementations, the filter dialog box pops up automatically when there are too many sub-nodes at the current location in the hierarchy. In some implementations, a threshold number (e.g., 10 or 20) is predefined for the user interface or for a particular data set. In some implementations, the threshold is based on the total number of displayed nodes instead of the number of sub-nodes at one specific node. In some implementations, the threshold is the maximum number of nodes that can be displayed within the user interfaced based on the current size of the data window or panel. In some implementations, the threshold is configurable. In some implementations, filters are, optionally, applied to the community hierarchy as well. That is, a user can apply filters in the referral hierarchy (as illustrated in FIGS. 5S-5BB) or to the hierarchy of community websites to which the traffic is being referred (i.e., the destination hierarchy). This is particularly valuable when there are many categories or boards within a community. The referral hierarchy shown in FIG. 5Z is merely illustrative of a hierarchy (e.g., different groups and sub-groups of referral destinations) that exists in a second data-set dimension (e.g., the dimension of referral destinations) and in some circumstances other hierarchies are used in an analogous manner.

FIG. 5CC provides a simple example of a community 5438 with five sub-nodes “Forums” 5392, “Blogs” 5394, “Ideas” 5396, “Tribal Knowledge Base (TKB)” 5398, and “Events” 5400. Each of these sub-nodes has a respective set of a small number of sub-nodes. When the total number of nodes and sub-nodes in a data set dimension is small enough for the user interface, the filters are, optionally, not needed. However, when the number of nodes and sub-nodes in the second column is large, the same filtering as shown with respect to the nodes in the first column are, optionally, applied.

FIG. 5DD illustrates that selecting a node or sub-node, such as sub-node 5440 in the first column, anywhere within the hierarchy prompts the interface to display a graph of all of the referral traffic involving that node. In FIG. 5DD, only a node in the first column is selected, as a result, the lines representing referral traffic from the selected node in the first column to each of the nodes/sub-nodes shown in the second column are displayed. In some implementations, the thicknesses of the lines would indicate the respective amounts of traffic going to each of the sub-nodes in the second column. In this conceptual figure, the exact counts of the referral traffic have been omitted, and the widths of the curves have not been adjusted to indicate the amount of traffic to or from each node/sub-node. As illustrated previously with respect to FIGS. 5B-5R, additional nodes can be selected (within either hierarchy/column), and the interface will display the traffic between the selected nodes.

FIG. 6 illustrates examples of a data flow and architecture supporting the level 1, level 2 and/or level 3 data reporting and visualization. FIG. 6 is merely illustrative, and in actual implementations, more or fewer components are, optionally, used than those shown. In addition, some components optionally support a different set of functions than those described.

As shown in FIG. 6, application 5602 (“LIA”) provides the platform that supports one or more online communities, e.g., online communities associated with various business entities (e.g., retailers, cosmetic companies, consumer electronics companies, etc.). The users (e.g., users 102 in FIG. 1) of the online communities generate content (e.g., blog posts, reviews, comments, ratings, answers, replies, etc.), and data related to their interactions (e.g., posting, rating content, registration, replying to questions, providing comments, etc.) with the communities are stored in persistent log 5604. Persistent log 5604 is scalable, fault tolerant, and has built-in redundancy to guard against failures. Persistent log 5604 stores all the data associated with the user interaction with the online communities (e.g., community interaction logs 324, forum data 326 and the like are stored at a database server 118 that is associated with online community 100 as shown in FIGS. 1 and 3). The data includes, for example, data related to the “who”, “what”, “when”, and “where” aspects of the user interactions with the online communities. In addition, attributes information (related to the “how” aspect) is also stored in association with the “what” aspect of the data.

In some implementations, as shown in FIG. 6, metrics and attributes are defined by the designer of application 5602, and the definitions of the metrics and attributes are stored in a definition store 5606. In general, metrics are raw data concerning various actions or interactions performed by the users of the communities. Examples of metrics include page views, posts, kudos, answers, replies, registrations, comments, logins, etc. Attributes generally refer to the context information (e.g., device type, time, community name, topics, keywords, etc.) associated with particular actions or metrics. Sometimes, attributes also include environmental variables associated with particular actions or metrics. In some implementations, the definitions of metrics and attributes are stored as definition files, and definitions files can be modified, added, and/or deleted from the definition store 5606.

In some implementations, as shown in FIG. 6, based on the definitions of metrics and attributes, some standard aggregation 5608 are defined and stored. Examples of standard aggregations 5608 include total views, average number of posts per day, average response time, total number of posts, etc. These standard aggregations are simple computations (e.g., additions, multiplications, averages, etc.) based on the raw interaction data recorded for the communities and stored in the persistent logs 5604. The standard aggregations 5608 can be easily defined using various scripting language, and can be retrieved by queries.

In some implementations, as shown in FIG. 6, standard aggregations computed based on actual data recorded on the communities are stored as standard view data 5610. The standard view data 5610 can be quickly retrieved and displayed to a viewer (e.g., a community manager, stakeholder and/or executive). As shown in FIG. 6, metric explorer 5612 (e.g., the user interfaces described with reference to FIGS. 9-005 to 9-141 of U.S. Provisional Patent Application No. 61/734,927, filed Dec. 7, 2012, entitled “Systems and Methods for Presenting Analytic Data” and FIGS. 5B-5DD above), and widgets (e.g., the widgets described above with reference to FIGS. 9-207 to 9-236 of U.S. Provisional Patent Application No. 61/734,927, filed Dec. 7, 2012, entitled “Systems and Methods for Presenting Analytic Data”) and dashboard 5614 are supported by a general purpose visualization engine 5616. The metric explorer 5612, widgets and dashboard 5614, and the general purpose visualization engine 5616 access the standard view data 5610 directly and present the standard aggregations and raw metrics to the user, e.g., using the example user interfaces described earlier. One advantage of computing and storing the standard view data 5610 is fast response time during interaction with the metric explorer 5612 and the widget and dashboard 5614, as indicated in FIG. 6.

In some implementations, in addition to the standard aggregations, more complex analytics and metrics (also referred to as advanced analytics 5618) are defined, as shown in FIG. 6. For example, advanced analytics algorithms are used to compute various community health factors (e.g., CHI, FEI, etc.). Other advanced analytics include metrics for measuring influence of users, hotness of topics, etc. Typically, these more advanced analytics are not simple aggregations of raw interactions data recorded on the communities. Instead, more complex algorithms (e.g., statistical methods, machine learning methods, non-linear analysis, etc.) are used to derive these analytics. Typically, these analytics have a non-linear relationship with the various metrics used to derive the analytics. In addition, these advanced analytics typically are based on a large number (e.g., hundreds) of standard metrics.

In some implementations, the definitions of the advanced analytics 5618 (also referred to as “derivative definitions”) are stored with the metric definitions and attribute definitions in the definition store 5606. In some implementations, the advanced analytics algorithms are implemented using various computing languages (e.g., matlab, Java, etc.), as shown in FIG. 6. In some implementations, as shown in FIG. 6, persistent log 5604 and standard view data 5610 both serve as the data sources for computing the advanced analytics 5618. In some implementations, data derivatives 5620 computed based on actual metrics data according to the advanced analytics algorithms are stored with the standard view data 5610, for quick retrieval and presentation by the metric explorer 5612 and widget and dashboard 5614, as shown in FIG. 6.

In some implementations, as shown in FIG. 6, an offline data derivative archive 5622 is implemented to store prior versions of the data derivatives. In some implementations, the advanced analytics algorithms are continuously improved and revised overtime, and the data derivatives 5620 derived using the older versions of the advanced analytics algorithms are stored in the offline data derivative archive 5622 with the their respective version numbers. In some implementations, the data stored in the offline data derivatives archive 5622 are used to evaluate the effectiveness of the advanced analytics algorithms, and provide benchmarking and comparison data for evaluating and validating the algorithms.

As described in earlier parts of the specification and shown in FIG. 6, a customer intelligence center 5624 is used to provide integrated data service for community managers, and stakeholders and executives of business entities providing the online communities. The customer intelligence center 5624 is an integrated suite of services, providing the metric explorer, and widgets and dashboard for users to selectively review data associated with various online communities. As described earlier, the customer intelligence center 5624 (e.g., as illustrated by the user interfaces shown in FIGS. 9-150 to 9-161 of U.S. Provisional Patent Application No. 61/734,927, filed Dec. 7, 2012, entitled “Systems and Methods for Presenting Analytic Data”) not only provides standard, predefined metrics, and advanced analytics to the users, but also allows users to define customized metrics and customized user interfaces (e.g., metrics explorers, widgets and dashboards), customized reports and visualizations. In addition, the customer intelligence center 5624 also allows a user (e.g., community manager, stakeholder, and executive) to establish a notification system which sends an alert to the user when certain predefined trigger conditions have been met. For example, a trigger condition can be a threshold value of a particular metric or data derivative, and/or a combination of different threshold values for a number of metrics and data derivatives. The customer intelligence center 5624 access data stored in the standard view data 5610 and data derivatives 5620 to support its functions.

Since the customer intelligence center 5624 allows a user to customize the metrics, aggregations, and data derivatives that can be reviewed in the customer intelligence center 5624. The definitions 5626 of the customized metrics, aggregations, and data derivatives are stored with the standard aggregations 5608. In some implementations, a metric/attribute to script/query translator 5628 is implemented and used to provide an interface between the customer intelligence center 5624 and the customized aggregations 5626 and standard aggregations 5608.

In some implementations, the data calculated according to the customized metrics, aggregations, and analytics are stored as custom view data 5630. The custom view data 5630, standard view data 5610, and data derivatives 5620 (e.g., as stored in database server(s) 118) together serve as the data source for the reports and visualizations shown to users via the metric explorer and the standard and customized widgets and dashboards (e.g., the user interfaces described with reference to FIGS. 9-005 to 9-236 of U.S. Provisional Patent Application No. 61/734,927, filed Dec. 7, 2012, entitled “Systems and Methods for Presenting Analytic Data” and FIGS. 5B-5DD above). By storing the custom view data 5630, standard view data 5610, and data derivatives 5620, the responsiveness for the metric explorer and widgets and dashboards can be improved.

In some implementations, as shown in FIG. 6, definitions of the customized metrics, aggregations, widgets, and reports are stored in the definition store 5606, together with the definitions of standard metrics, attributes, and data derivatives. In some implementations, the stored definitions of the customized metrics, aggregation, widgets, and reports are reused by their creators. In some implementations, the stored definitions of the customized metrics, aggregation, widgets, and reports searchable and utilized by other users who, in some circumstances, will find them useful for reviewing their own community data in the customer intelligence center 5624.

In some implementations, the customer intelligence center 5624 allows a user (e.g., community manager, stakeholder and/or executives) to establish the criteria for having notifications sent to a user-specified recipient. In some implementations, a general purpose notification system 5632 is utilized. A user defines the trigger event for the notification, the type of information to be included in the notification, the format of the notification, and a rate limit for the notification. Examples of trigger events for a notification include a community health indicator (e.g., CHI) value having dropped below a threshold value, an average response time for a particular sub-community having reached a maximum acceptable value, new registration rate having dropped below a minimum threshold value, etc. In some implementations, a trigger event can also be a combination of several conditions. In some implementations, the user is allowed to specify what information the notification would include. For example, the user can require the notification to include actual values of certain metrics and data derivatives, and/or visualizations and reports of certain metrics/time periods/channels/users, etc. In some implementations, the notifications are provided as an email alert, a text message, or some other kinds of messages. In some implementations, the user is allowed to specify a preferred rate limit for the notifications. For example, a rate limit of 2 notifications per day means even if a trigger event has been met more than twice in a particular day, only two notifications are sent to the user. In some implementations, each notification sent to the user can include an aggregated report related to multiple trigger events that have occurred in the past but not yet been notified to the user due to the rate limit. The rate limit allows the user to control the number of notifications received each month/day/hour, so that the user is not overwhelmed by too many notification messages.

As described earlier, the metric explore 5612 is able to present community data on various metrics filtered by various attributes. The metrics explorer 5612 obtains the definitions of the metrics and attributes from the definition store 5606. In addition to the interaction data recorded on the online communities supported by the platform application 5602, in some implementations, the metric explorer also provides data from other third-party channels 5634, as shown in FIG. 6. For example, data from third-party channels, such as third-party blogs, micro-blogs (e.g., twitter), social networks (e.g., Facebook, Google+, etc.), other social media (e.g., Youtube, Flickr, etc.), can be selectively presented in the metric explorer as well. The integration of social media channels 5634 into the metric explorer allows users to have a broader view of the impact and health of their online communities and business objectives.

In some implementations, as shown in FIG. 6, persistent logs 5604 also store data related to the usage of the customer intelligence center 5624, such as what customization the user has made to the metrics explorer, widgets and dashboards, what customized metrics, aggregations, reports, and widgets have been created by the customers, what notifications have been implemented by the customers, how frequently each types of metrics, aggregations, data derivatives are reviewed by the customers, what times periods the customers typically wish to review, etc. The data related to the usage of the customer intelligence center 5624 can be used to generate additional definitions that are useful for many other customers. The usage data can also be used to determine what standard and customer view data to compute and store for easy and fast access. The usage data is in general useful for improving the overall efficiency and usability of the customer intelligence center.

The foregoing description, for purpose of explanation, has been described with reference to specific implementations. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The implementations were chosen and described in order to best explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to best utilize the invention and various implementations with various modifications as are suited to the particular use contemplated.

Claims

1. A method, comprising:

at a computing device with a display: displaying, in a data display panel, a plurality of nodes associated with a data set, wherein: the plurality of nodes include a first set of one or more nodes and a second set of one or more nodes distinct from the first set of nodes; and the data set includes information indicating a magnitude of a relationship between nodes in the first set of nodes and nodes in the second set of nodes; receiving a first input corresponding to selection of a first node in the first set of nodes; and in response to receiving the input, displaying, in the data display panel, a visual indication of a magnitude of a relationship between the first node and one or more nodes in the second set of nodes.

2. The method of claim 1, wherein the first set of nodes correspond to a first set of divisions in a first data-set dimension.

3. The method of claim 2, wherein the second set of nodes correspond to a second set of divisions in a second data-set dimension different from the first data-set dimension.

4. The method of claim 1, further comprising, in response to receiving the first input, displaying a visual indication of the magnitude of the relationship between the first node and a plurality of nodes in the second set of nodes.

5. The method of claim 1, further comprising, in response to receiving the first input, displaying a visual indication of the magnitude of the relationship between the first node and each node in the second set of nodes.

6. The method of claim 1, further comprising:

receiving a second input corresponding to selection of a second node the second set of nodes for which a respective visual indication of a magnitude of a relationship between the first node and the second node is displayed; and

in response to receiving the second input, ceasing to display the respective visual indication of the magnitude of the relationship between the first node and the second node.

7. The method of claim 1, further comprising, while the first node is selected:

receiving a third input corresponding to selection of a second node the second set of nodes for which a respective visual indication of a magnitude of a relationship between the first node and the second node is not displayed; and

in response to receiving the third input, displaying a respective visual indication of the magnitude of the relationship between the first node and the second node.

8. The method of claim 1, further comprising:

receiving a fourth input corresponding to a request to display sub-nodes of a second node the second set of nodes for which a respective visual indication of a magnitude of a relationship between the first node and the second node is displayed; and

in response to receiving the fourth input: displaying a plurality of sub-nodes of the second node, including a first sub-node and a second sub-node; displaying a visual indication that indicates a magnitude of relationship between the first node and the first-sub node of the second node; and displaying a visual indication that indicates a magnitude of relationship between the first node and the second-sub node of the second node.

9. The method of claim 1, wherein:

the visual indication of the magnitude of the relationship between the first node and one or more nodes in the second set of nodes includes a visual indication of a magnitude of a relationship between the first node and a second node the second set of nodes; and

the method further comprises: receiving a fifth input corresponding to a request to display sub-nodes of the first node; and in response to receiving the fifth input: displaying a plurality of sub-nodes of the first node, including a first sub-node and a second sub-node; displaying a visual indication that indicates a magnitude of relationship between the first-sub node of the first node and the second node; and displaying a visual indication that indicates a magnitude of relationship between the second-sub node of the first node and the second node.

10. The method of claim 9, wherein:

the first node includes more sub-nodes than can be displayed in the data display panel; and

displaying the plurality of sub-nodes of the first node includes displaying a subset of the sub-nodes associated with the first node.

11. The method of claim 10, wherein the subset of the sub-nodes associated with the first node is selected automatically based on predefined criteria.

12. The method of claim 10, wherein the subset of the sub-nodes associated with the first node is selected based on user-provided criteria.

13. The method of claim 1, wherein:

the visual indication of the magnitude of the relationship between the first node and one or more nodes in the second set of nodes includes a connector between the first node and the one or more nodes in the second set of nodes;

a first connector width of the connector proximate to the first node corresponds to an aggregate magnitude of the relationship between the first node and the one or more nodes in the second set of nodes; and

a second connector width of the connector proximate to a respective node in the second set of nodes corresponds to a magnitude of the relationship between the first node and the respective node.

14. The method of claim 1, wherein the relationship between the first node and the one or more second nodes is a flow of network traffic.

15. A computing device, comprising:

one or more processors; and

memory;

one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: displaying, in a data display panel, a plurality of nodes associated with a data set, wherein: the plurality of nodes include a first set of one or more nodes and a second set of one or more nodes distinct from the first set of nodes; and the data set includes information indicating a magnitude of a relationship between nodes in the first set of nodes and nodes in the second set of nodes; receiving a first input corresponding to selection of a first node in the first set of nodes; and in response to receiving the input, displaying, in the data display panel, a visual indication of a magnitude of a relationship between the first node and one or more nodes in the second set of nodes.

16. The device of claim 15, wherein the one or more programs include instructions for, in response to receiving the first input, displaying a visual indication of the magnitude of the relationship between the first node and each node in the second set of nodes.

17. The device of claim 15, wherein the one or more programs include instructions for:

receiving a second input corresponding to selection of a second node the second set of nodes for which a respective visual indication of a magnitude of a relationship between the first node and the second node is displayed; and

in response to receiving the second input, ceasing to display the respective visual indication of the magnitude of the relationship between the first node and the second node.

18. A non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device with one or more processors cause the device to:

display, in a data display panel, a plurality of nodes associated with a data set, wherein: the plurality of nodes include a first set of one or more nodes and a second set of one or more nodes distinct from the first set of nodes; and the data set includes information indicating a magnitude of a relationship between nodes in the first set of nodes and nodes in the second set of nodes;

receive a first input corresponding to selection of a first node in the first set of nodes; and

in response to receiving the input, display, in the data display panel, a visual indication of a magnitude of a relationship between the first node and one or more nodes in the second set of nodes.

19. The non-transitory computer readable storage medium of claim 18, wherein the one or more programs include instructions which, when executed by the computing device, cause the device to, in response to receiving the first input, display a visual indication of the magnitude of the relationship between the first node and each node in the second set of nodes.

20. The non-transitory computer readable storage medium of claim 18, wherein the one or more programs include instructions which, when executed by the computing device, cause the device to:

receive a second input corresponding to selection of a second node the second set of nodes for which a respective visual indication of a magnitude of a relationship between the first node and the second node is displayed; and

in response to receiving the second input, cease to display the respective visual indication of the magnitude of the relationship between the first node and the second node.