Device, Method and User Interface for Emphasizing Divisions in Data
A device with one or more processors and memory displays, in a data display panel, a first representation of data from a data set. The data is selected in accordance divisions in a first data-set dimension and divisions in a second data-set dimension, and is organized to emphasize the divisions in the first data-set dimension. The device receives an input corresponding to switching the dimension selector from a first state to a second state. In response to receiving the input, the device displays, in the data display panel, a second representation of a second subset of data from the data set, where the second subset of data is selected from the data set in accordance with the divisions in the first data-set dimension and the divisions in the second data-set dimension, and is organized to emphasize the divisions in the second data-set dimension.
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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 Displaying Relationships between Different Sets of Data,” filed ______, (Attorney Docket No. 070407-5010US); and U.S. patent application Ser. No. ______, “Device, Method and User Interface for Switching between Graphical Representations of Data,” filed ______, (Attorney Docket No. 070407-5011US), which are incorporated by reference herein in their entirety.
TECHNICAL FIELDThe disclosed implementations relate generally to methods of data visualization.
BACKGROUNDAn 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.
SUMMARYIt 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 first representation of a first subset of data from a data set. The first subset of data is selected from the data set in accordance with both a first plurality of divisions in a first data-set dimension and a second plurality of divisions in a second data-set dimension, where the first data-set dimension is different from the second data-set dimension. The first representation of the data is organized to emphasize the first plurality of divisions in the first data-set dimension. The method further includes receiving an input corresponding to switching a current state of the dimension selector from a first state to a second state, and, in response to receiving the input, displaying, in the data display panel, a second representation of a second subset of data from the data set. The second subset of data is selected from the data set in accordance with both the first plurality of divisions in the first data-set dimension and the second plurality of divisions in the second data-set dimension. The second representation of the data is organized to emphasize the second plurality of divisions in the second data-set dimension.
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.
FIGS. 5A-5VV illustrate methods of presenting, selecting, and changing views of data in accordance with some implementations.
Like reference numerals refer to corresponding parts throughout the several views of the drawings.
DESCRIPTION OF IMPLEMENTATIONSReference 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.
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
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- 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. 5A-5VV below and 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. 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.
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- 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. 5A-5VV below and 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. 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
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
The device displays (402), in a data display panel, a first representation of a first subset of data from a data set. The first subset of data is selected (404) from the data set in accordance with both a first plurality of divisions in a first data-set dimension and a second plurality of divisions in a second data-set dimension. The first data-set dimension is different from the second data-set dimension (e.g., the first subset of data is selected so as to exclude data that is not in at least one division of the first plurality of divisions and at least one division of the second plurality of divisions). In some implementations, the first plurality of divisions in the first data-set dimension and the second plurality of divisions in the second data-set dimension are selected (406) in response to inputs received from a user (e.g., as shown in FIGS. 5I-5VV). For example, the first plurality of divisions and the second plurality of divisions are selected based on selection of options in data-selection interfaces adjacent to the data display field, as described herein with reference to method 400.
The first representation of the data is organized (408) to emphasize the first plurality of divisions in the first data-set dimension (e.g., the first plurality of divisions are prominently displayed in the visualization of the data displayed in the data display panels, for example, by displaying the divisions as separate bars in a bar chart or separate lines in a line graph). In some implementations, the first representation visually aggregates (410) data by the second plurality of divisions (e.g., only data from the second plurality of divisions is included in the subset of data) and visually separates the aggregated data in accordance with the first plurality of divisions. For example, the first representation is a line graph with different lines corresponding to different divisions in the user dimension of the data set. In some implementations, the first representation visually groups (412) data by the first plurality of divisions and shows subdivisions corresponding to the second plurality of divisions. For example, the first representation is a bar chart with different bars corresponding to different divisions in the first user dimension of the data set as shown in
In some implementations, the device displays (414) a visual indication of the first plurality of divisions in a first data-selection interface (e.g., panel) extending in a first direction from the data display panel. For example, the data-selection interface includes options corresponding to divisions in the data-source dimension is described herein with reference to the region on the top side of the central data display panel in FIGS. 5I-5VV. In some implementations, the device displays (416) a visual indication of the second plurality of divisions in a second data-selection interface (e.g., panel) extending in a second direction from the data display panel, where the first direction is different from the second direction. For example, the data-selection interface including options corresponding to divisions in the user dimension is described herein with reference to the region on the right side of the central data display panel in FIGS. 5I-5VV. In some implementations, the device displays (418) a dimension priority icon (e.g., user interface element 5132 in FIGS. 5I-5VV) that indicates that the first plurality of divisions in the first data-set dimension are emphasized over the second plurality of divisions in the second data-set dimension.
After displaying the first representation of the first subset of the data in the data-display panel, the device receives (420) an input (e.g., from the input device) corresponding to switching a current state of the dimension selector from a first state to a second state. In response to receiving the input, the device displays (422), in the data display panel, a second representation of a second subset of data from the data set. In some implementations, the first subset of data is (424) the same as the second subset of data. In some implementations, the first subset of data is organized or displayed differently from the second subset of data. The second subset of data is selected (426) from the data set in accordance with both the first plurality of divisions in the first data-set dimension and the second plurality of divisions in the second data-set dimension (e.g., the first subset of data is selected so as to exclude data that is not in at least one division of the first plurality of divisions and at least one division of the second plurality of divisions). The second representation of the data is organized (428) to emphasize the second plurality of divisions in the second data-set dimension (e.g., as shown in FIGS. 5I-5VV).
In some implementations, the second representation visually aggregates (430) data by the first plurality of divisions (e.g., only data from the second plurality of divisions is included in the subset of data) and visually separates the aggregated data in accordance with the first plurality of divisions. For example, the first representation is a line graph with different lines corresponding to different divisions in the data-source dimension of the data set, as shown in
In some implementations, prior to receiving the input, the subdivisions corresponding to the second plurality of divisions include a first subdivision shown in a first color and a second subdivision shown in a second color, and after receiving the input, the subdivisions corresponding to the first plurality of divisions include (434) a first subdivision shown in the first color and a second subdivision shown in the second color. Thus, in some implementations, the colors used to show subdivisions for the representation of the data remain the same, but prior to detecting the input, the colors are assigned to divisions in the first data-set dimension and after detecting the input, the same colors are assigned to divisions in the second data-set dimension. For example, as shown in FIG. 5JJ, user divisions are displayed in blue, orange, green and purple, while the data-source divisions are emphasized by grouping the user divisions into bar graphs by data-source division. In contrast, in FIG. 5KK, data-source divisions are displayed in blue, orange, green and purple, while the user divisions are emphasized by grouping the data-source divisions into bar graphs by user division.
In some implementations, prior to detecting the input, the device displays a dimension priority icon (e.g., user interface element 5132 in FIGS. 5I-5VV) that indicates that the first plurality of divisions in the first data-set dimension are emphasized over the second plurality of divisions in the second data-set dimension (e.g., by pointing at the first data-selection interface, as shown in FIG. 5JJ). In some implementations, after detecting the input, the device changes (436) display of the dimension priority icon to indicate that the second plurality of divisions in the second data-set dimension are emphasized over the first plurality of divisions in the first data-set dimension. For example the device changes display of the dimension priority icon so that it points at the second data-selection interface, as shown in FIG. 5KK.
It should be understood that the particular order in which the operations in
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. 5A-5VV 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.
FIGS. 5A-5VV illustrate various implementations that provide a simpler and more effective user interface for displaying level 1 data and level 2 data. The level 1 data and the level 2 data are examples of a multi-dimensional data set, where data units within the multi-dimensional data set can be selectively displayed in the user interface using various data selection interfaces (or data selectors). In some implementations, each data set dimension represents a respective aspect of the data set. For example, dimensions of the data set can be related to the “who”, “what”, “when”, and “where” aspects of the data set, respectively. In some implementations, each data set dimension further includes multiple “divisions”, where each division represents a filter for a subset of the available data. For example, divisions in the “who” dimension can include various categories, roles, and/or ranks of users. Divisions in the “what” dimension can include various types of raw metrics and analytics (collectively called “metrics”). Divisions in the “what” dimension can be further filtered or limited by a “how” dimension, which provides attributes or context for the data in the “what” dimension. Divisions in the “when” dimension can include various time periods and/or time ranges. Divisions in the “where” dimension can include various sources or channels from which the data is collected. For example, the “where” dimension can include various channels (e.g., community, various other third-party social media sites, and so on) from which the metrics data is collected. Sometimes, the “where” dimension also include subdivisions representing sub-topics and/or sub-communities within a community or data channel. The “who”, “what”, “when”, and “where” data-set dimensions are merely illustrative for the kinds of data-set dimensions that can be represented by the data selection interfaces shown below. Not all dimensions need to be present in a data set, and not all dimensions present in the data set need to have a respective data selection interface in various implementations.
In
In
Creating a separate graph for each (node, user selection) combination is both visually hard to understand, and generally does not correspond to what a user wants to see. Instead, preferred implementations enable grouping of the selected data. For example, if a user wants a separate graph for each selected role, then the data for the selected nodes can be grouped together for each selected role. Conversely, if the user wants a graph for each of the nodes, then the data for all of the selected roles can be grouped together for each of the selected nodes. In some implementations, this is implemented using a “Group By” arrow button 5132 (i.e., an example of a dimension selector). In some of some implementations, there is a label “Group By” 5130 adjacent to the arrow button 5132 to provide more information to the user using the user interface shown in
As will be shown in later figures, when the group by arrow 5132 points to the user selector 5026, there is a single graph for each of the selected roles. The data for each graph is limited to the selected nodes rather than using data for the entire community.
Similarly, as will be shown in later figures, when the group by arrow 5132 points to the channel selector 5018, as illustrated in
In some implementations the “Group By” label 5130 and group by arrow 5132 only appear when there are multiple selections for both nodes and users/user groups.
In
In
In
In
As illustrated in
In some implementations, the user interface provides an option to view the bar graphs as relative percentages. In some implementations, there is an indicator or button 5198 that identifies the current view (either by numbers or relative percentages). In
In
In
FIGS. 5AA and 5BB illustrate changes in the displayed data in response to a user flipping back and forth between total post counts (in FIG. 5AA) and relative percentages of the total post count (in FIG. 5BB). In FIG. 5CC, a user has switched the “group by” arrow 5132 back to grouping by role, so the bar graphs in the data panel 5028 update accordingly, each bar corresponding to one of the selected roles, and showing the relative percent of posts from each of the selected nodes.
FIGS. 5DD-5II illustrate how some implementations display data for an individual bar segment as a user hovers the cursor 5038 over the bar segment. In FIG. 5DD, a user has unselected the VIP role 5182, so there are only bar graphs for roles “Advanced Studio” 5174, “Category Expert” 5176, and “HiFi_Employee” 5178. In FIG. 5EE, a user has hovered the cursor/pointer 5038 over the region 5202 at least a predetermined length of time (e.g., 1 second or 2 seconds). The region 5202 corresponds to role “Advanced Studio” 5174 and node “Engage with HiFi” 5172. The displayed percentage “3.3%” identifies the percentage of posts by “Advanced Studio” users 5174 for the “Engage with HiFi” node/forum 5172. In FIGS. 5FF-5HH, the user hovers the cursor 5038 over the regions 5204, 5206, and 5208, and in each case when the cursor is hovering over a respective region, the corresponding percentage for the respective region is displayed by the device displaying the metric explorer user interface. These are all for the role “Advanced Studio” 5174, each corresponding to a node or forum. In this implementation, the correspondence is directly visible because the color of each bar segment (5204, 5206, and 5208) corresponds to the highlighting color of the node (5184, 5186, and 5188 respectively). Because of rounding, each of the percentages, the total of the percentages is not exactly 100% (3.3%+56.5%+29.4%+10.7%=99.9%). In some implementations, the user interface employs rounding techniques so that the sum is guaranteed to be 100%. FIGS. 5II and 5JJ illustrate the display of the numeric percentages (37.9% and 14.8%) for the “CodeCave Blog” node 5188 for the user roles “Category Expert” 5176 and “HiFi_Employee” 5178. The percentages display generally over the corresponding regions 5210 and 5212.
FIGS. 5KK-5OO illustrate the display of relative percentages when the data is grouped by nodes rather than user roles. In FIG. 5KK, a user has switched the “group by” indicator 5132, and thus the bars are displaying vertically, with region sizes as a percentage of posts for each node. FIGS. 5LL-5OO illustrate hovering over regions 5214, 5216, 5218, and 5220 with cursor 5038. Each of these regions corresponds to the user role “Category Expert” 5176, but the percentage is based on the nodes 5172, 5184, 5186, and 5188 respectively.
Comparison of FIGS. 5II-5OO illustrates how the relative percent for a bar region depends on how the data is viewed. In FIG. 5II, bar region 5210 corresponds to the “CodeCave Blog” node 5188 and the “CategoryExpert” user role 5176, and this region is 37.9% of the total post count for the user role 5176. In FIG. 5OO, the bar region 5220 corresponds to the “CodeCave Blog” node 5188 and the “CategoryExpert” user role 5176 (just like bar region 5210 in FIG. 5II), but in FIG. 5OO, the bar region 5220 represents 73.9% of the post count for the “CodeCave Blog” node 5188.
FIGS. 5PP-5VV further illustrate display of numeric data for a bar region when the cursor hovers over a region for a predetermined period of time. In FIG. 5PP, the user role 5182 is reselected, and thus the bars in the bar graphs include this user role. As shown in the upper left corner of the data panel 5128, the view indicator/button 5200 indicates that the device (e.g., device 110) displaying the user interface shown in FIGS. 5PP-5VV is displaying bar graphs with each bar region shown as a relative percent. In this view mode, all of the bars have the same length, corresponding to 100%. In FIG. 5QQ, a user has switched the view mode, now viewing data based on the counts. (The currently selected metric is “post count” as shown in the metric selector panel 5020.) In some implementations, the selector 5200 itself acts as a button, so that a user can switch view modes by clicking on the button 5200. In some implementations, the view mode is selected from a menu or toolbar icon (not illustrated). In some implementations, the menu or toolbar selection options are in addition to the indicator button 5200. As shown in FIG. 5QQ, the view mode indicator 5198 shown that the view mode is by number/count. In some implementations, the symbol “#” indicates viewing of data by counts. As illustrated in FIG. 5QQ, when viewing data by counts rather than percentages, the bars of unequal length when different quantities of data are associated with the different nodes (e.g., different aggregate post counts for “Engage with HIFI,” “HIFI Ideas,” “Coder Ideas,” and “CodeCave Blog”).
FIGS. 5RR-5UU illustrate the display of post counts for the VIP role 5182 for each of the four nodes 5172, 5184, 5186, and 5188. The user interface displays the count for a region when the cursor hovers over the region for a predetermined length of time, and the user interface removes the displayed count for a region when a user moves the cursor 5038 outside of the region. In some implementations, there is a slight delay before removing the displayed number (e.g., half a second). In some implementations, the predetermined length of time that constitutes “hovering” is configurable, either for an individual user using the user interface, or for a community, or for other logical groups of individuals who use the user interface. As shown in FIGS. 5RR and 5SS, the numeric display of the count for a region will, in some circumstances, exceed the size of the region. In these circumstances, the numeric display is placed as much as possible within the boundary of the region so that the user viewing the user interface can readily correlate the data to a region. As illustrated in FIG. 5VV, when the cursor 5038 moves to a location that is not above any graph regions, no numeric counts are displayed.
As shown in
In some implementations, as shown in
In some implementations, as shown in
In some implementations, as shown in
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
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
In some implementations, as shown in
As described in earlier parts of the specification and shown in
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 (e.g., as described above with reference to FIGS. 5A-5VV) and the standard and customized widgets and dashboards (e.g., the user interfaces described with reference to FIGS. 9-142 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”). 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
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
In some implementations, as shown in
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 first representation of a first subset of data from a data set, wherein: the first subset of data is selected from the data set in accordance with both a first plurality of divisions in a first data-set dimension and a second plurality of divisions in a second data-set dimension, wherein the first data-set dimension is different from the second data-set dimension; and the first representation of the data is organized to emphasize the first plurality of divisions in the first data-set dimension; receiving an input corresponding to switching a current state of the dimension selector from a first state to a second state; and in response to receiving the input, displaying, in the data display panel, a second representation of a second subset of data from the data set, wherein: the second subset of data is selected from the data set in accordance with both the first plurality of divisions in the first data-set dimension and the second plurality of divisions in the second data-set dimension; and the second representation of the data is organized to emphasize the second plurality of divisions in the second data-set dimension.
2. The method of claim 1, wherein the first subset of data is the same as the second subset of data.
3. The method of claim 1, wherein the first plurality of divisions in the first data-set dimension and the second plurality of divisions in the second data-set dimension are selected in response to inputs received from a user.
4. The method of claim 1, comprising, displaying a visual indication of the first plurality of divisions in a first data-selection interface extending in a first direction from the data display panel.
5. The method of claim 4, comprising, displaying a visual indication of the second plurality of divisions in a second data-selection interface extending in a second direction from the data display panel, wherein the first direction is different from the second direction.
6. The method of claim 1, further comprising:
- prior to detecting the input, displaying a dimension priority icon that indicates that the first plurality of divisions in the first data-set dimension are emphasized over the second plurality of divisions in the second data-set dimension; and
- after detecting the input, changing display of the dimension priority icon to indicate that the second plurality of divisions in the second data-set dimension are emphasized over the first plurality of divisions in the first data-set dimension.
7. The method of claim 1, wherein the first representation visually aggregates data by the second plurality of divisions and visually separates the aggregated data in accordance with the first plurality of divisions.
8. The method of claim 7, wherein the second representation visually aggregates data by the first plurality of divisions and visually separates the aggregated data in accordance with the first plurality of divisions.
9. The method of claim 1, wherein the first representation visually groups data by the first plurality of divisions and shows subdivisions corresponding to the second plurality of divisions.
10. The method of claim 9, wherein the second representation visually groups data by the second plurality of divisions and shows subdivisions corresponding to the first plurality of divisions.
11. The method of claim 10, wherein:
- prior to receiving the input, the subdivisions corresponding to the second plurality of divisions include a first subdivision shown in a first color and a second subdivision shown in a second color; and
- after receiving the input, the subdivisions corresponding to the first plurality of divisions include a first subdivision shown in the first color and a second subdivision shown in the second color.
12. 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 first representation of a first subset of data from a data set, wherein: the first subset of data is selected from the data set in accordance with both a first plurality of divisions in a first data-set dimension and a second plurality of divisions in a second data-set dimension, wherein the first data-set dimension is different from the second data-set dimension; and the first representation of the data is organized to emphasize the first plurality of divisions in the first data-set dimension; receiving an input corresponding to switching a current state of the dimension selector from a first state to a second state; and in response to receiving the input, displaying, in the data display panel, a second representation of a second subset of data from the data set, wherein: the second subset of data is selected from the data set in accordance with both the first plurality of divisions in the first data-set dimension and the second plurality of divisions in the second data-set dimension; and the second representation of the data is organized to emphasize the second plurality of divisions in the second data-set dimension.
13. The device of claim 12, wherein the first subset of data is the same as the second subset of data.
14. The device of claim 12, wherein the one or more programs include instructions for, displaying a visual indication of the first plurality of divisions in a first data-selection interface extending in a first direction from the data display panel.
15. The device of claim 14, wherein the one or more programs include instructions for, displaying a visual indication of the second plurality of divisions in a second data-selection interface extending in a second direction from the data display panel, wherein the first direction is different from the second direction.
16. 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 first representation of a first subset of data from a data set, wherein: the first subset of data is selected from the data set in accordance with both a first plurality of divisions in a first data-set dimension and a second plurality of divisions in a second data-set dimension, wherein the first data-set dimension is different from the second data-set dimension; and the first representation of the data is organized to emphasize the first plurality of divisions in the first data-set dimension;
- receive an input corresponding to switching a current state of the dimension selector from a first state to a second state; and
- in response to receiving the input, display, in the data display panel, a second representation of a second subset of data from the data set, wherein: the second subset of data is selected from the data set in accordance with both the first plurality of divisions in the first data-set dimension and the second plurality of divisions in the second data-set dimension; and the second representation of the data is organized to emphasize the second plurality of divisions in the second data-set dimension.
17. The non-transitory computer readable storage medium of claim 16, wherein the first subset of data is the same as the second subset of data.
18. The non-transitory computer readable storage medium of claim 16, comprising instructions which, when executed by the computing device, cause the computing device to display a visual indication of the first plurality of divisions in a first data-selection interface extending in a first direction from the data display panel.
19. The non-transitory computer readable storage medium of claim 18, comprising instructions, which when executed by the computing device, cause the computing device to display a visual indication of the second plurality of divisions in a second data-selection interface extending in a second direction from the data display panel, wherein the first direction is different from the second direction.
Type: Application
Filed: Dec 5, 2013
Publication Date: Jun 19, 2014
Applicant: LITHIUM TECHNOLOGIES, INC. (EMERYVILLE, CA)
Inventor: Michael Wu (Oakland, CA)
Application Number: 14/098,509
International Classification: G06F 3/0481 (20060101);