Patent ID: 12204552

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques, instruction sequences, and computing machine program products that embody illustrative embodiments of the disclosure. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide an understanding of various embodiments of the inventive subject matter. It will be evident, however, to those skilled in the art, that embodiments of the inventive subject matter may be practiced without these specific details. In general, well-known instruction instances, protocols, structures, and techniques are not necessarily shown in detail.

In various example embodiments, investigation of datasets can be enhanced through sequenced dataset reduction using sequenced filter templates. Reducing datasets using filters can result in widely varying results, many of which may not be useful for the type of analysis being conducted. For example, a user investigating a dataset trying to find the source of a food poisoning outbreak may implement different filters (e.g., filtering by distance, years, past outbreak data) to reduce the dataset to find the source of the outbreak. However, which filters are applied and in what order can drastically change the resulting dataset. For instance, an inexperienced user may apply a distance filter early in the analysis and inadvertently filter out the source of the outbreak.

These issues can be addressed using a sequenced filter template that reduces a dataset in a specific way—applying particular filters in a specified order—to yield a resultant dataset that more readily highlights the desired target to be identified (e.g., a source of a food poisoning outbreak). A sequenced filter template comprises a set of filters to be applied to a dataset in a specified sequence. The ordering of the sequence may, for example, be configured by an expert investigator that understands how to properly reduce a dataset to yield useful results. The expert investigator may, for example, be an individual that is familiar with past investigations and understands how to properly drill-down a set of data with multiple filters to yield a reduced dataset that readily identifies target sources.

To create datasets for analysis, in some embodiments, a browser may be configured to detect whether a webpage is parsable, and generate a parse interface to assist parsing useful datasets from the webpage. In some embodiments, the browser parse functionality is implemented using a browser plugin. The plugin detects the website of a webpage displayed within the browser and determines whether the website is parsable. If the website is parsable, the browser plugin parses the webpage and displays a parse user interface, which displays input fields auto-populated with parsed data from the webpage. The user may modify, remove, or add additional data to the input fields and submit directly to the backend system, which may in turn receive the data and store it as part of the dataset for analysis.

FIG.1is a block diagram illustrating various functional components of a query sequencer network architecture, according to some example embodiments. A networked system102provides server-side functionality via a network104(e.g., the Internet or wide area network (WAN)) to one or more client devices110. In some implementations, a user (e.g., user106) interacts with the networked system102using the client device110.FIG.1illustrates, for example, a browser parser112(e.g., a browser), and a data visualizer114executing on the client device110. The client device110includes the browser parser112, and the data visualizer114, alone, together, or in any suitable combination. AlthoughFIG.1shows one client device110, in other implementations, the network architecture100comprises multiple client devices.

In various implementations, the client device110comprises a computing device that includes at least a display and communication capabilities that provide access to the networked system102via the network104. The client device110comprises, but is not limited to, a remote device, work station, computer, Internet appliance, hand-held device, wireless device, portable device, wearable computer, cellular or mobile phone, Personal Digital Assistant (PDA), smart phone, tablet, ultrabook, netbook, laptop, desktop, multi-processor system, microprocessor-based or programmable consumer electronic, game consoles, set-top box, network Personal Computer (PC), mini-computer, and so forth. In an example embodiment, the client device110comprises one or more of a touch screen, accelerometer, gyroscope, biometric sensor, camera, microphone, Global Positioning System (GPS) device, and the like.

The client device110communicates with the network104via a wired or wireless connection. For example, one or more portions of the network104comprises an ad hoc network, an intranet, an extranet, a Virtual Private Network (VPN), a Local Area Network (LAN), a wireless LAN (WLAN), a Wide Area Network (WAN), a wireless WAN (WWAN), a Metropolitan Area Network (MAN), a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a cellular telephone network, a wireless network, a Wireless Fidelity (WI-FI®) network, a Worldwide Interoperability for Microwave Access (WiMax) network, another type of network, or any suitable combination thereof.

In some example embodiments, the client device110includes one or more of the applications (also referred to as “apps”). In some example embodiments, the browser parser112and data visualizer114access the various systems of the networked system102via a web interface supported by a web server122. In some example embodiments, the browser parser112and data visualizer114access the various services and functions provided by the networked system102via a programmatic interface provided by an Application Program Interface (API) server120. The data visualizer114is a dataset visualization tool that is configured to manipulate datasets and display visualizations that allow a human user to detect patterns, trends, or signals that would not previously have been detectable (e.g., signals that would otherwise be lost in noise). The data visualizer114is configured to work with a data visualizer backend system150, which performs backend operations for the client side data visualizer114. In some example embodiments, the data visualizer114is run from a browser as a web service and the data visualizer backend system150serves as the web service for the front end, e.g., the data visualizer114.

The query sequencer115manages the sequenced filter template functionality for the data visualizer114. In some embodiments, the query sequencer115is configured as a plugin that plugs into the data visualizer114to enhance the filtering capabilities of the data visualizer114. As discussed in further detail below, in some embodiments, the modules and functionalities of the query sequencer115may be directly integrated into the data visualizer114. The browser parser112is an Internet browser that is configured to parse webpages, and submit information obtained from parsing to a backend system for storage in the dataset. In some embodiments, the browser parser112is an Internet browser with a plugin that is configured to perform the parse operations.

Users (e.g., the user106) comprise a person, a machine, or other means of interacting with the client device110. In some example embodiments, the user106is not part of the network architecture100, but interacts with the network architecture100via the client device110or another means. For instance, the user106provides input (e.g., touch screen input or alphanumeric input) to the client device110and the input is communicated to the networked system102via the network104. In this instance, the networked system102, in response to receiving the input from the user106, communicates information to the client device110via the network104to be presented to the user106. In this way, the user106can interact with the networked system102using the client device110.

The API server120and the web server122are coupled to, and provide programmatic and web interfaces respectively to, one or more application server140. The application server140can host a data visualizer backend system150configured to support the data visualizer114, each of which comprises one or more modules or applications and each of which can be embodied as hardware, software, firmware, or any combination thereof. The application server140are, in turn, shown to be coupled to one or more database server124that facilitate access to one or more information storage repositories or database126. In an example embodiment, the database126are storage devices that store database objects parsed from browser parser112, as well as store datasets to be analyzed by the data visualizer114.

Additionally, a third party application132, executing on third party server130, is shown as having programmatic access to the networked system102via the programmatic interface provided by the API server120. For example, the third party application132, utilizing information retrieved from the networked system102, supports one or more features or functions on a website hosted by the third party. The third party website, for example, provides webpages which can be parsed using the browser parser112.

Further, while the client-server-based network architecture100shown inFIG.1employs a client-server architecture, the present inventive subject matter is, of course, not limited to such an architecture, and can equally well find application in a distributed, or peer-to-peer, architecture system, for example. The various systems of the application server140(e.g., the data visualizer backend system150) can also be implemented as standalone software programs, which do not necessarily have networking capabilities.

FIG.2is a block diagram illustrating various functional modules that form a query sequencer115, according to some example embodiments. In various example embodiments, the query sequencer115comprises a plugin engine210, a user interface engine220, a template library,230, a filter engine240, and a query constructor engine250. The plugin engine210is a communication interface that integrates the query sequencer115into the data visualizer114though a plugin specification of the data visualizer114.

The user interface engine220is configured to generate and display user interfaces for implementing the sequenced filter templates. The template library230is a library of available sequenced filter templates for selection by a user. Each of the templates may be configured by an expert user to drill down and solve different types of investigative problems. For example, one template in the template library230can drill-down into a set of restaurant distribution and logistics data to detect the source of a food poisoning outbreak. In some example embodiments, each of the sequenced filter templates specifies a sequence in which to apply filters to a dataset in order to produce a reduced dataset useful for analysis.

Though an investigative scenario involving food poisoning is discussed here for illustrative purposes, it is appreciated that each sequence filter template can be configured for widely varying investigative purposes, e.g., detecting bank fraud, analyzing shipping/logistics problems, tracking humanitarian aid, detecting cyber threats, and other analysis problems.

The filter engine240manages the filters applied by templates of the template library230. Each of the filters may have custom configured functionality that may be further refined by customization parameters by the non-expert user at runtime of a selected filter. For example, a years filter may be preconfigured by the expert to return datasets matching a year range 1990-1999 (10 years), while a customization parameter may change the span of years, e.g., 1995-1999 (5 years), shift the year range 2000-2009 (10 years, shifted), or other changes.

The query constructor engine250receives or retrieves the sequenced filter template from the template library230, receives filter data including filter logic and customizable parameter data as available, and constructs sequenced query code for submission to the data visualizer114or submission to the data visualizer backend system150. The sequenced query code can be structured query language, or other types of programmatic language to query a database.

One technical advantage of query sequencer115implementing sequenced filter templates is that non-expert users (e.g., users applying a configured sequenced filter template) can generate a reduced dataset that is similar to or the same as a reduced dataset generated by an expert investigative user. An additional technical advantage stems from the usability. Non-expert users may be of at least two types: a user that does not know the correct ordering of filters to apply, or a user that does not know how to produce the query code. In some cases, a non-expert user may not know the correct ordering of filters and may not know how to produce the query code for a sequenced filter. The query sequencer115handles both of these shortcomings by using expert-created filter templates to handle order sequencing, and user interfaces and the query constructor engine250to allow a non-expert user to product query code for a sequenced template filter without having to write query code.

FIG.3is a block diagram illustrating various functional modules that form a data visualizer114, according to some example embodiments. As discussed, the data visualizer114may have the plugin functionality of the query sequencer115built into the application framework of the data visualizer114. Thus, in these embodiments, the data visualizer114may comprise some or all of the components of the query sequencer115, including the user interface engine220, the template library230, the filter engine240, and the query constructor engine250.

The data visualizer114may further include additional components used to communicate with other network components, manipulate data, and generate visualizations of data for analysis. As illustrated in the example embodiment ofFIG.3, the data visualizer114comprises a backend API300, a visualization library270, and database engine275. The backend API300is configured to connect to the data visualizer backend system150to submit sequenced queries and receive results. The visualization library270includes a plurality of visualizations that may be applied to datasets and displayed on a display device (e.g., of client device110) to allow an investigative user to investigate data and detect patterns and sources previously undetectable. The database engine275is a database service that can receive queries and retrieve corresponding data from a database. In some embodiments, the database engine275is implemented in the client device110, where the client device110stores datasets locally, while in some example embodiments, where the dataset to be reduced is not local to the client device110, the database engine275may be integrated in the data visualizer backend system150or database server124.

FIG.4is a flow diagram illustrating a method for generating a reduced dataset using a sequenced filter template, according to some example embodiments.

The method400may be embodied in machine-readable instructions for execution by a hardware component (e.g., a processor) such that the operations of the method400may be performed by the data visualizer114; accordingly, the method400is described below, by way of example with reference thereto. However, it shall be appreciated that the method400may be deployed on various other hardware configurations and is not intended to be limited to the data visualizer114. At operation410, the user interface engine220generates a display of a selected sequenced filter template on a display screen of client device110. The selected sequence template may be selected from the template library230. The display of the selected sequenced filter template comprises fields for customization parameters to modify the functionality of the filters, as described above.

At operation420, the plugin engine210receives customization parameters (e.g., entered by the user106using a user interface presented on the client device110). In some example embodiments, customization parameters modify the scope or effect of a filter. For example, a filter may be a year range filter that filters out data not in a given range. A customization parameter can change the range in duration (e.g., last five years, last 24 hours), modify the starting and ending points of the filter, or other modifications. Further details of customization parameters are discussed below with reference toFIGS.6,7, and8A-8D.

At operation430, the query constructor engine250generates query code using the selected filter template. The query constructor engine250generates each filter, modifies each filter according to received customization parameters, and arranges the filters into a sequence in the query.

At operation440, the query comprising the plurality of filters modified by customization parameters is applied to a dataset to filter data per each filter to result in a reduced dataset. In some example embodiments, the reduced dataset is a dataset honed by a user to more readily display patterns and find target sources. At operation450, the visualization library270displays the reduced dataset using one or more visualizations. For example, the visualization library270may display the reduced dataset as graph data having nodes connected by edges.FIGS.8E-Hillustrate example visualizations that may be used to display the reduced dataset, according to some example embodiments.

The flow diagram inFIG.4shows a method400where the client device110is capable of applying the constructed query to the dataset to generate the reduced dataset. In some embodiments, the client device110is not configured to apply the query to the dataset. For example, the data visualizer114may be implemented as a cloud service on a browser running from the client device110. In those example embodiments, the data visualizer114may transmit the constructed query to the application server140for further processing.

FIG.5is a flow diagram illustrating a method500for generating a reduced dataset using a sequenced filter template across a network, according to some example embodiments. The client device110contacts the application server140through the network104and the application server140and the database server124are executed from separate physical machines. In some embodiments, the application server140is a server specially configured to receive requests from the data visualizer114and function as a backend web service provider. In some embodiments, the database server124is a commercially available database server (e.g., Oracle database server) that is configured to receive queries in a specified SQL type. In those example embodiments, the data visualizer backend system150are configured to receive queries from the data visualizer114and translate them to the SQL type of the database server124. In some embodiments, the application server140or data visualizer backend system150has the database functionality of database server124integrated into the application server140or data visualizer backend system150. Thus, it is appreciated that the columns divisions of the method500are illustrated strictly as an example, and other configurations are possible per implementation.

At operation505, the user interface engine220generates a display of a selected sequenced filter template on a display screen of client device110. The selected sequence template may be selected from the template library230. The display of the selected sequenced filter template comprises fields for customization parameters to modify the functionality of the filters.

At operation510, the plugin engine210receives customization parameters from the user106. At operation515, the query constructor engine250generates query code using the selected filter template. The query constructor engine250generates code for each filter, modifies each filter according to received customization parameters, and arranges the filters into a sequence in the query. The query may then be passed through the backend API300, over network104, to the application server140. At operation520, the data visualizer backend system150receives the sequenced query. At operation525, the data visualizer backend system150translates the query to a code format for the database server124if necessary. For example, the query received at operation520may be in a proprietary query language and database server124may be an off-the-shelf commercially available platform (e.g., an Oracle Database system) that uses structured query language incompatible with the proprietary query language. In such an example embodiment, at operation525the query is translated from the proprietary query language format to the query language of database server124(e.g., Oracle SQL), such that the ordering of the filter sequence and parameters of the original query generated at operation515are retained. The backend API300transmits the query (e.g., translated query) to the database server124. At operation530, the database server124apply the query to a dataset in database126to generate the reduced dataset. At operation535, the database server124transmits the reduced dataset to the application server140. At operation540, the data visualizer backend system150transmits the reduced dataset to the backend API300of the data visualizer114on client device110.

At operation545, the backend API300stores the reduce dataset on memory local to the client device110, according to some example embodiments. At operation550, the visualization library270uses the stored reduced dataset to generate a visualization and display the visualized reduced dataset on the display screen of client device110. The user106may then view and manipulate the reduced dataset to identify target data (e.g., food poisoning source).

FIG.6is a flow diagram illustrating a method for applying filters of a sequenced filter template, according to some example embodiments. As illustrated, in some example embodiments, the flow diagram depicted inFIG.6can be implemented as a subroutine for operation440of method400, which is an operation where the database engine275applies the sequenced filter template to the dataset to generate the reduced dataset. The flow diagram depicted inFIG.6shows a method of filtering using multiple loops or iterations. It is appreciated that in some example embodiments, the constructed query is configured to apply all filters in the sequence in one operation.

At operation605, the database engine275receives the constructed sequenced query. At operation610, the database engine275identifies the first filter in the sequence of the sequenced filter template. At operation615, the database engine275applies the filter to the dataset to generate a first reduced dataset. At operation620, the database engine275determines whether there are additional filters in the sequenced query. If there are additional filters in the sequence, then at operation625, the next filter in the sequence is identified and the process goes to operation615, where the next filter is applied. When there are no more filters in the sequenced, the operation continues to operation630where the dataset reduced by one or more filters is returned or output as the reduced dataset.

FIG.7is a diagram illustrating a user interface view702and a constructed query view708of a sequenced filter template, according to some embodiments. The user interface view702may be displayed to the user106on the display screen of the client device110to allow the user106to view the sequence and understand the flow of the sequenced filter. As illustrated in the user interface view702, the dataset704the initial unfiltered dataset. A series of four right-pointing arrows show example filters and the order of the sequence, from left to right. The filters are applied according to the sequence and specified parameters to generate reduced dataset706. The functionality of each filter is discussed in further detail below, with reference toFIG.7, according to some example embodiments.

The constructed query view708is a logical view of the query code constructed by the query constructor engine250, according to some example embodiments. As illustrated, the query may be implemented using structured query language (SQL) designed to access the database126, though it is appreciated that the filtering code implemented can be other programming languages, according to some embodiments. The expert investigative user may be a programmer or code developer that is fluent or experienced in writing the query or filter code. Once the query code is written and stored to the query sequencer as a sequence filter template, the non-expert user can use the query code through user interface objects (e.g., checkboxes, drag and drop elements) as shown in further detail below.

The example query code beings with “SELECT * FROM table1”, where “SELECT” and “FROM” are statements of the query and “table1” is an example dataset to be reduced. Each of the four filters represented by arrows corresponds to filter code, as indicated by the double-sided arrows. In particular, the left-most arrow, a “medium” filter, corresponds to first filter code710, which comprises additional query code (e.g., WHERE, AND, OR, etc.), as specified by the expert user. As illustrated, the first filter code710comprises parameter data712that includes one or more customization parameters that can be customized by the non-expert user when implementing the sequenced filter template. Similarly, the second filter (a “year” filter) corresponds to second filter code714with one or more parameter data716, the third filter (an “area” filter) corresponds to the third filter code718with parameter data720, and the fourth filter (a “distributor” filter) corresponds to the fourth filter code722, having one or more parameter data724. Each of the filters can be implemented using the loop operation ofFIG.6, according to some example embodiments. In some example embodiments, each of the filters can be nested and applied at once in the sequence shown in user interface view702without looping or iterating.

FIGS.8A-Dillustrate user interfaces of a data visualizer implementing sequenced template filters, according to some example embodiments. As illustrated inFIG.8A, data visualizer user interface800comprises a toolbar area805, a visualization area810to display data (e.g., datasets, reduced datasets), a sequence flow area815that shows the filters to be applied per the selected template, and a sequenced filter user interface830as generated by the user interface engine220of the query sequencer115. As illustrated, the sequenced filter user interface830may display different template options820in a drop-down menu819. The template options820may be provided by the template library230, according to some embodiments. Assuming the user selects the “Poison Analyzer” from the template options820, the sequence flow area815will display the filters to be applied for the selected template, and further display the sequence or order of the filters to be applied. The right-most filter may be indicated as optional through graying out, or through use of broken lines. Optional filters are filters that the expert-user designated as not necessary for investigative analysis, but may yield beneficial results in some cases, so the optional filters remain selectable by the non-expert user.

FIG.8Billustrates a data visualizer user interface800of the data visualizer114, with the selected sequenced template filter displayed in the sequenced filter user interface830. As illustrated, the selected filter is the poison analyzer template831, comprising a first filter835, for the “medium” of food contaminate; a second filter840for the year range to be considered; a third filter845for the geographic area to be analyzed; and a fourth filter850, which is an optional filter for analyzing distributors. Each of the filter's835-850have checkboxes with options selectable by the non-expert user. Each of the utilized checkboxes modifies the customization parameters of the filter, e.g., parameter data712ofFIG.7, according to some example embodiments. As illustrated, the fourth filter850is left blank, with no selection being made, and no data entered into the illustrated input field. As such, the to-be-generated query comprises three levels, and skips the optional fourth filter850.

Upon selecting the submit sequenced query855, the filters and parameters of the selected poison analyzer template831are applied to the dataset to generate a reduced dataset (e.g., reduced dataset706). As discussed, in some embodiments, the data visualizer114can directly apply the sequence filter template to the dataset using the database engine275. In other example embodiments, the sequenced filter template query code is constructed by the query constructor engine250on the client device110, then transmitted to the data visualizer backend system150for application to the dataset and generation of the reduced dataset, as discussed above with reference toFIG.5.

FIG.8Cshows an example reduced dataset860that results from applying the poison analyzer template831to the dataset. As illustrated, the reduced dataset860is visualized in the visualization area810as a network graph comprising nodes that are connected by edges. Each of the nodes can correspond to different data entities, such as restaurant locations, or other parameters in the dataset. As illustrated, each of the nodes corresponds to a distributor, “Acme Distributor.” Because the reduced dataset860was generated by a specially configured sequenced filter template, the reduced dataset860will more readily identify target data. For example, the identified target node865here can be flagged as having the most network connections to other nodes, thus likely being the source of the food poisoning.

FIG.8Dshows an example reduced dataset870visualized as a network graph. In some example embodiments, network graphs (e.g., a social graph) depict a database item as a circle or “node”, which are connected by lines or “edges”). The reduced dataset870ofFIG.8Dwas generated by applying the optional fourth filter850. In particular, the fourth filter's850customization parameters were set to “Beta Co.” Thus, the reduced dataset870may not readily identify the source of the food poisoning because “Acme Distributor” would be filtered out by the fourth filter850. Thus, a non-expert user can defer to the selection of filter, the ordering of the filters, and any default parameters as arranged by the expert user; however, the non-expert user may still have the ability to modify the query away from the expert's selection through selecting different user interface objects.

FIGS.8E-8Hdepict different types of visualizations that may be used to display the reduced dataset, according to some example embodiments. The visualizations may stored and otherwise managed by visualization library. Upon a reduced dataset being generated, a user (e.g., non-expert user) can select a visualization from the visualization library270to display the data. In some embodiments, the expert investigative user may specify which visualization may be used to display the reduced dataset. For example, the expert user may know from experience that target data (e.g., source of an outbreak) may best be displayed in a network graph. Thus upon applying the sequenced filter template by the non-expert user, the data visualizer114generates the reduced dataset as described above, but further automatically displays the reduced dataset using the visualization specified by the non-expert user.

FIG.8Eillustrates a bar graph visualization872representation of displaying the reduced dataset, according to some example embodiment.FIG.8Fillustrates the reduced dataset displayed as a histogram visualization874. A histogram is similar to a bar graph, but generally a histogram illustrates data input as a continuum of ranges or range sets, whereas a bar graph displays data as separate categories.FIG.8Gillustrates the reduced dataset displayed as a distribution plot876.FIG.8Hillustrates the reduced dataset displayed as a pie chart878and a table879, according to some example embodiments.

With reference toFIGS.9-11B, the client device110can execute an Internet browser configured to use a browser parser112to retrieve data from webpages and store them as the dataset to be analyzed, as discussed above, and on some embodiments, the browser parser112is an Internet browser with a plugin that is configured to perform the parse operations.

FIG.9is a block diagram showing components provided within the browser parser112, according to some embodiments. In various example embodiments, the browser parser112comprises a browser plugin API910, a website parse template library920, a user interface engine930, a parse engine940, and a database API950. The browser plugin API910is a plugin programming interface that configures the browser parser112to work as a plugin or extension application for an Internet browser (e.g., Google Chrome, Microsoft Internet Explorer, Apple Safari, Mozilla Firefox). Upon the browser loading a webpage of a website, the browser plugin API910receives notification of which web site the webpage was provided. The website parse template library920comprises different parse templates for different websites. In some example embodiments, parse engine940determines whether there is a parse template for the current website in the website parse template library920. A parse template is a template configured to identify different fields of the source code of pages from the website. If there is a template in the website parse template library920, the parse engine940uses the template to parse the source code of the webpage and extract data from different fields. The user interface engine930generates a parse user interface with fields populated with data obtained from parsing the webpage. The data obtained from parsing the webpage can be submitted through the parse user interface to be stored as a database object having attribute values defined by the fields parsed. The database API950is configured to store the parsed object as the dataset through interfacing with the dataset management device, e.g., database server124or data visualizer backend system150.

FIG.10is a flow diagram illustrating a method1000for parsing a webpage to generate a dataset for analysis, according to some example embodiments. At operation1010, the browser parser112displays a webpage to the user106on a display screen of the client device110. At operation1020, the parse engine940receives, from the browser plugin API910, an identifier (e.g., URL) of the website served the current webpage. In operation1030, the parse engine940searches the website parse template library920to determine whether a parse template exists for the website. In some example embodiments, the website parse template library920maintains a look-up table comprising a list of which websites have parse templates and further directions on which template to load for which website.

If the parse engine940determines, at operation1030, that website parse template library920does not have a parse template for the website, then the browser parser112cannot parse the page and the process terminates as illustrated at operation1040. However, if it is determined that a parse template exists for the website, the parse engine940retrieves the parse template from the website parse template library920for processing. At operation1050, the parse engine940uses the parse template retrieved from the website parse template library920to parse the webpage. As discussed, a parse template is configured to identify fields and extract values from the source code of the page. For example, the source code of a webpage may include title field source code, such as “<title> sample title </title>”. The browser parser112identifies the field using the tags (<title>), and extracts the data enclosed in the tags (sample title). The data obtained from parsing the webpage (e.g., sample title) are then passed to the user interface engine930for further processing. At operation1060, the user interface engine930receives the parsed values and generates a user interface for display within the browser. The user interface displays a number of editable fields, each of which can be prepopulated with data parsed from the webpage. The user106can edit the data in the fields or enter new data into the field if none was parsed. At operation1070, the user106clicks a submit button on the generated user interface, which causes the database API950to transmit or otherwise store the webpage as an object in the dataset.

FIGS.11A-11Billustrate user interfaces of a browser parser for generating a dataset from webpages, according to some example embodiments. InFIG.11A, a browser1100comprising a toolbar area1105and an address bar1110is displayed. Through links or through directly inserting a URL into the address bar1110, the user106can cause the browser1100to load pages from different sites. For example, as illustrated, browser1100has loaded a webpage from www.acmeresearchpapers.com/chimera_534. The webpage contains an article on the “chimera virus”. The URL of the webpage is www.acmeresearchpapers.com. As illustrated, the webpage comprises an article title1115, article metadata1120(e.g., authors, publisher, year published), and article text1125. Upon loading the page, the browser plugin API910may display an active icon1130alerting the user106that pages from the Acme site are parsable. The user106may click on the active icon1130, which causes the parse engine940to parse the webpage according to parse template for the website.

FIG.11Billustrates a parse user interface1113generated by the user interface engine930in response to the user106clicking the active icon1130. As illustrated, the parse user interface1113may pop-up or fade in as an overlay in a different layer over the displayed webpage. The parse user interface1113comprises a plurality of input fields1150, including “title,” which was prepopulated from the article title1115; “year”, “author”, and “from,” which were prepopulated from the article metadata1120; and “keywords,” which was prepopulated from the most common words found in the article text1125. The input fields1150are modifiable by the user106to correct errors or change the information. For example, the user106may change the “year” from “2002” to “2008”, or delete the year value. The parse user interface1113further includes a submit button1155, which the user106may select to cause the database API950to store the webpage as a research paper database object having attributes including “title”, “year”, “author”, “from”, and “keywords”. The research paper database object can be stored in the dataset, which can be analyzed using the sequenced filter templates discussed above. In this way, the client device110is configured as an efficient streamlined investigation tool: collecting information through the browser parser112and analyzing datasets, which include the collected information through a data visualizer114enhanced by guide investigations by the query sequencer115.

FIG.12is a block diagram illustrating components of a machine1200, according to some example embodiments, able to read instructions from a machine-readable medium (e.g., a machine-readable storage medium) and perform any one or more of the methodologies discussed herein. Specifically,FIG.12shows a diagrammatic representation of the machine1200in the example form of a computer system, within which instructions1216(e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine1200to perform any one or more of the methodologies discussed herein can be executed. For example, the instructions1216can cause the machine1200to execute the flow diagrams ofFIGS.4,5,6, and10. Additionally, or alternatively, the instructions1216can implement the plugin engine210, the user interface engine220, the template library230, the filter engine240, query constructor engine250, the backend API300, the visualization library270, the database engine275, the browser plugin API910, the website parse template library920, the user interface engine930, the parse engine940, and the database API950, ofFIGS.2,3, and9, and so forth. The instructions1216transform the general, non-programmed machine into a particular machine programmed to carry out the described and illustrated functions in the manner described. In alternative embodiments, the machine1200operates as a standalone device or can be coupled (e.g., networked) to other machines. In a networked deployment, the machine1200may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine1200can comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a personal digital assistant (PDA), an entertainment media system, a cellular telephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions1216, sequentially or otherwise, that specify actions to be taken by the machine1200. Further, while only a single machine1200is illustrated, the term “machine” shall also be taken to include a collection of machines1200that individually or jointly execute the instructions1216to perform any one or more of the methodologies discussed herein.

The machine1200can include processors1210, memory/storage1230, and I/O components1250, which can be configured to communicate with each other such as via a bus1202. In an example embodiment, the processors1210(e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) can include, for example, processor1212and processor1214that may execute instructions1216. The term “processor” is intended to include multi-core processor that may comprise two or more independent processors (sometimes referred to as “cores”) that can execute instructions contemporaneously. AlthoughFIG.12shows multiple processors1210, the machine1200may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof.

The memory/storage1230can include a memory1232, such as a main memory, or other memory storage, and a storage unit1236, both accessible to the processors1210such as via the bus1202. The storage unit1236and memory1232store the instructions1216embodying any one or more of the methodologies or functions described herein. The instructions1216can also reside, completely or partially, within the memory1232, within the storage unit1236, within at least one of the processors1210(e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine1200. Accordingly, the memory1232, the storage unit1236, and the memory of the processors1210are examples of machine-readable media.

As used herein, the term “machine-readable medium” means a device able to store instructions and data temporarily or permanently and may include, but is not be limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, optical media, magnetic media, cache memory, other types of storage (e.g., Erasable Programmable Read-Only Memory (EEPROM)) or any suitable combination thereof. The term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store instructions1216. The term “machine-readable medium” shall also be taken to include any medium, or combination of multiple media, that is capable of storing instructions (e.g., instructions1216) for execution by a machine (e.g., machine1200), such that the instructions, when executed by one or more processors of the machine1200(e.g., processors1210), cause the machine1200to perform any one or more of the methodologies described herein. Accordingly, a “machine-readable medium” refers to a single storage apparatus or device, as well as “cloud-based” storage systems or storage networks that include multiple storage apparatus or devices. The term “machine-readable medium” excludes signals per se.

The I/O components1250can include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components1250that are included in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components1250can include many other components that are not shown inFIG.12. The I/O components1250are grouped according to functionality merely for simplifying the following discussion, and the grouping is in no way limiting. In various example embodiments, the I/O components1250can include output components1252and input components1254. The output components1252can include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The input components1254can include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or other pointing instruments), tactile input components (e.g., a physical button, a touch screen that provides location and force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like.

In further example embodiments, the I/O components1250can include biometric components1256, motion components1258, environmental components1260, or position components1262among a wide array of other components. For example, the biometric components1256can include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram based identification), and the like. The motion components1258can include acceleration sensor components (e.g., an accelerometer), gravitation sensor components, rotation sensor components (e.g., a gyroscope), and so forth. The environmental components1260can include, for example, illumination sensor components (e.g., a photometer), temperature sensor components (e.g., one or more thermometers that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., a barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensor components (e.g., machine olfaction detection sensors, gas detection sensors to detect concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment. The position components1262can include location sensor components (e.g., a Global Positioning System (GPS) receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like.

Communication can be implemented using a wide variety of technologies. The I/O components1250may include communication components1264operable to couple the machine1200to a network1280or devices1270via a coupling1282and a coupling1272, respectively. For example, the communication components1264include a network interface component or other suitable device to interface with the network1280. In further examples, communication components1264include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, BLUETOOTH® components (e.g., BLUETOOTH® Low Energy), WI-FI® components, and other communication components to provide communication via other modalities. The devices1270may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a Universal Serial Bus (USB)).

Moreover, the communication components1264can detect identifiers or include components operable to detect identifiers. For example, the communication components1264can include Radio Frequency Identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as a Universal Product Code (UPC) bar code, multi-dimensional bar codes such as a Quick Response (QR) code, Aztec Code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, Uniform Commercial Code Reduced Space Symbology (UCC RSS)-2D bar codes, and other optical codes), acoustic detection components (e.g., microphones to identify tagged audio signals), or any suitable combination thereof. In addition, a variety of information can be derived via the communication components1264, such as location via Internet Protocol (IP) geo-location, location via WI-FI® signal triangulation, location via detecting a BLUETOOTH® or NFC beacon signal that may indicate a particular location, and so forth.

In various example embodiments, one or more portions of the network1280can be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), the Internet, a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a WI-FI® network, another type of network, or a combination of two or more such networks. For example, the network1280or a portion of the network1280may include a wireless or cellular network, and the coupling1282may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or other type of cellular or wireless coupling. In this example, the coupling1282can implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standard, others defined by various standard setting organizations, other long range protocols, or other data transfer technology.

The instructions1216can be transmitted or received over the network1280using a transmission medium via a network interface device (e.g., a network interface component included in the communication components1264) and utilizing any one of a number of well-known transfer protocols (e.g., Hypertext Transfer Protocol (HTTP)). Similarly, the instructions1216can be transmitted or received using a transmission medium via the coupling1272(e.g., a peer-to-peer coupling) to devices1270. The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying the instructions1216for execution by the machine1200, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.

Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein.

Although an overview of the inventive subject matter has been described with reference to specific example embodiments, various modifications and changes may be made to these embodiments without departing from the broader scope of embodiments of the present disclosure. Such embodiments of the inventive subject matter may be referred to herein, individually or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single disclosure or inventive concept if more than one is, in fact, disclosed.

The embodiments illustrated herein are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. The Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled.

As used herein, the term “or” may be construed in either an inclusive or exclusive sense. Moreover, plural instances may be provided for resources, operations, or structures described herein as a single instance. Additionally, boundaries between various resources, operations, modules, engines, and data stores are somewhat arbitrary, and particular operations are illustrated in a context of specific illustrative configurations. Other allocations of functionality are envisioned and may fall within a scope of various embodiments of the present disclosure. In general, structures and functionality presented as separate resources in the example configurations may be implemented as a combined structure or resource. Similarly, structures and functionality presented as a single resource may be implemented as separate resources. These and other variations, modifications, additions, and improvements fall within a scope of embodiments of the present disclosure as represented by the appended claims. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.