Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: A machine-implemented method for supporting a natural language user request against a user's personal data cloud can include a machine receiving the natural language user request from the user, determining a semantic interpretation of the natural language user request, querying a semantically-indexed, integrated knowledge store based on the semantic interpretation, and responding to the natural language user request by displaying results of the querying, wherein the results correspond to an item within the user's personal data cloud.

Abstract: A machine-implemented method for supporting a natural language user request against a user's personal data cloud can include a machine receiving the natural language user request from the user, determining a semantic interpretation of the natural language user request, querying a semantically-indexed, integrated knowledge store based on the semantic interpretation, and responding to the natural language user request by displaying results of the querying, wherein the results correspond to an item within the user's personal data cloud.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: A machine-implemented method for supporting a natural language user request against a user's personal data cloud can include a machine receiving the natural language user request from the user, determining a semantic interpretation of the natural language user request, querying a semantically-indexed, integrated knowledge store based on the semantic interpretation, and responding to the natural language user request by displaying results of the querying, wherein the results correspond to an item within the user's personal data cloud.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: Scorecard data including scorecard views, and the like are exported to a presentation application for generating a presentation based on the scorecard data. Briefing book definitions are generated based on the scorecard data, default parameters and user-defined parameters for providing a user selections for the presentations. Using composite objects, scorecard and report views may be reformatted, resized, laid out, and paginated according to the presentation preferences. Once the user selections are received the presentation may be rendered generating charts based on the data, grouping and breaking down views, incorporating unstructured data, and the like.

Abstract: Architecture for slicing data defined on both tabular data sources and in OLAP (online analytical processing) multidimensional data sources by time relative to the current date simultaneously with the same time intelligence (TI) filter on a dashboard page. The architecture employs a simple time period specification (STPS) language used to specify time periods in monitoring server TI filters, and key performance indicator (KPI) filters. The architecture maps all time dimensions to a common set of time aggregations (hierarchy) and to a common calendar.

Abstract: A machine-implemented method for supporting a natural language user request against a user's personal data cloud can include a machine receiving the natural language user request from the user, determining a semantic interpretation of the natural language user request, querying a semantically-indexed, integrated knowledge store based on the semantic interpretation, and responding to the natural language user request by displaying results of the querying, wherein the results correspond to an item within the user's personal data cloud.

Abstract: User interface that enables a user to manipulate visual representations of non-linear business logic to define and run data-centric “what-if” simulations. The visual interface models non-linear calculations based on the mapping of an input value in a non-linear domain to a score range, which score range can be continuous or non-continuous. The interface presents variable graphical indicator representations, as well as proximity and sensitivity visualizations. Different models can be developed and tested using live data feeds and aggregated dependent data feeds. The visual interface employs recursive scoring, and facilitates collaborative interface interaction.

Abstract: An apparatus and computer-implemented method for enhancing a calendar view on a device comprising extracting data from a user's linked data store, processing the data to recognize one or more entities within the data, inferring correlations between the entities, supplementing existing calendar entries on the user's device with the inferred correlations, creating new entities and displaying one or more actions to the user based on the context of a selected calendar entry.

Abstract: A machine-implemented method for supporting a natural language user request against a user's personal data cloud can include a machine receiving the natural language user request from the user, determining a semantic interpretation of the natural language user request, querying a semantically-indexed, integrated knowledge store based on the semantic interpretation, and responding to the natural language user request by displaying results of the querying, wherein the results correspond to an item within the user's personal data cloud.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: Scorecard associated content is provided to limited user interfaces of desktop visualization applications on a user's computing device (e.g. a mobile computing device, an ultra-mobile computing device, a personal digital assistant, an in-car computing system, and a tablet computing device) for delivery of personalized and scalable metrics. Users are enabled to set up personalized metric views based on predefined or user-defined desktop visualization applications employing indicators, partial report views, audio, video, and the like. Data delivery attributes from local or remote data sources can be set for deployment of the desktop visualization applications in a service based architecture. Computing device visualization applications may also be used to activate local or remote applications for various scorecard operations.

Abstract: Persistent annotations are created on a scorecard that combines multi-dimensional as well as fixed value data. The annotations uniquely defined by the scorecard view definition and by the retrieved scorecard data, are independent of the data's dimensionality enabling persistence of the annotations with the data and definition even when the scorecard is reconfigured. The annotations may include a “bubble-up” feature, where a hierarchical structure of the scorecard is inherited by the annotations. Threaded discussions and updated document lists are enabled around the annotations with appropriate permissions and/or credentials.

Abstract: Data retrieval from data sources associated with elements of a scorecard is optimized by combining the queries for the same data sources into a single query. The querying process is further optimized based on an attribute and/or condition of a data source. Query parameters and execution schedule may be adjusted based on the elements, data sources, or subscriber identity. Retrieved data is cached at element, combination of elements, data source, or scorecard level for rapid and efficient building and maintenance of scorecards.