Abstract: An illustrative embodiment provides a computer-implemented process for navigation through historical stored interactions associated with a multi-user view that receives a previously saved multi-user view, wherein the multi-user view comprises a set of artifact attributes, receives an identified filter from a user, and presents a filtered view to the user. The process further determines whether to amend the filtered view, and responsive to a determination to amend the filtered view, generates an amended view from the filtered view, and responsive to a determination to save the amended view, saves the amended view as one of a new view or an updated view.

Abstract: A method, a computer program product, and an information handling system is provided for identifying a causal relationship between metrics performing steps. The steps include receiving a correlation significance Sij between a metric Mi and a metric Mj; receiving a mutability attribute Ai for the Mi and a mutability attribute Aj for the metric Mj from a data source; and identifying the metric Mi causing the metric Mj with the Sij if only if the Ai is immutable and the Aj is mutable.

Abstract: Display data in a data graphical user interface (GUI) on a display device and display, by a processor, a control GUI on the display device representing the data. The control GUI includes a range defining visual element, corresponding to a data value, that is moveable via input received by the control GUI and defines two ranges of the data. The processor displays indicia associated with the range defining visual element indicating the corresponding data value. The processor receives input from the control GUI indicating the range defining visual element has been moved. In response, the processor visually emphasizes data in the data GUI having data values in a selected one of the defined data range.

Abstract: A method, system and computer program product for evaluating the interestingness of correlated data. The fields of a dataset are classified by tagging the fields in terms of real world concepts. A correlation analysis on the dataset is performed to generate a correlation coefficient for each pair of fields of correlated data items. An “ontological distance” between the tagged concepts for each pair of fields of correlated data items represented as nodes in the ontology is determined. A score is generated indicating an interestingness of correlation for each pair of fields of correlated data items based on the correlation coefficient and the ontological distance between the tagged concepts for each pair of fields of correlated data items. By utilizing the ontological distance with the correlation analysis to determine the interestingness of correlation, correlations that may not be obvious to users and unexpectedly correlated may be identified.

Abstract: Dynamically generating a model for a report includes presenting a user with source data in a unitary structure including rows and columns of data. Selections received from the user include first, second and third ones of the columns. A nesting structure of the second column data relative to the first column data indicates the second column data is of a dimension that is a level of the first column data. A lack of nesting structure of the third column data relative to the first column data indicates the third column data is of a dimension that is separate from a dimension of the first column data. In response to receiving the user selections, a computer system builder module generates a snowflake-schema-based, analytic processing model, wherein for the selected first, second and third columns the builder module automatically creates respective first, second and third tables and table mapping structures.

Abstract: An application executing on a target platform renders one or more widgets of one or more widget types that are specified by a document including, for each respective widget type from the one or more widget types: render, by a first widget renderer for the respective widget type, the one or more widgets of the respective widget type upon determining that the application includes the first widget renderer for the respective widget type, and render, by a second widget renderer for the respective widget type, the one or more widgets of the respective widget type upon determining that the application does not include the first widget renderer for the respective widget type. The second widget renderer may be different from the first widget renderer.

Abstract: Dynamically generating a model for a report includes presenting a user with source data by a source application, wherein the source data is available for producing a report. Selections are received from the user indicating portions of the source data to potentially include in the report. In response to receiving the user selections, a computer system builder module generates an analytic processing model for the user selected portions of the source data.

Abstract: A method, system and computer program product for assisting a user in selecting a desired hashtag. The text of a message that is composed by a user is parsed to determine the topic of interest, the sentiment of the message and the target audience. The historical and current communications for the target audience are evaluated to identify hashtags that are trending on the topic of the composed message. Furthermore, the historical and current communications for the target audience are evaluated to identify hashtags that match the desired sentiment of the composed message. The propagation speeds of the identified hashtags will then be identified. A recommended hashtag is then generated to be populated in the composed message that will increase a propagation speed of the composed message and/or emphasize the sentiment of the composed message based on these evaluations and the propagation speeds of the hashtags.

Abstract: A method, system and computer program product for evaluating the interestingness of correlated data. The fields of a dataset are classified by tagging the fields in terms of real world concepts. A correlation analysis on the dataset is performed to generate a correlation coefficient for each pair of fields of correlated data items. An “ontological distance” between the tagged concepts for each pair of fields of correlated data items represented as nodes in the ontology is determined. A score is generated indicating an interestingness of correlation for each pair of fields of correlated data items based on the correlation coefficient and the ontological distance between the tagged concepts for each pair of fields of correlated data items. By utilizing the ontological distance with the correlation analysis to determine the interestingness of correlation, correlations that may not be obvious to users and unexpectedly correlated may be identified.

Abstract: Techniques are described for data model augmentation. In one example, techniques include receiving a data contribution to a data model, receiving meta information about the data contribution, mapping the received data contribution and received meta information to the data model; and dynamically generating an augmentation of the data model based on the mapping.

Abstract: A mechanism is provided in a data processing system for statistical clustering inferred from natural language to drive relevant analysis. The mechanism receives a natural language text from a user and processes the natural language text to identify an entity of interest and a focus of statistical analysis. The mechanism performs a follow-up question and answer conversation with the user to receiving from the user one or more driving factor values for the one or more driving factors. The mechanism determines at least one cluster of entities matching the one or more driving factor values and generates at least one data visualization of the data in the corpus for the focus of statistical analysis having a scope that is narrowed based on the at least one cluster of entities matching the one or more driving factor values.

Abstract: Display data in a data graphical user interface (GUI) on a display device and display, by a processor, a control GUI on the display device representing the data. The control GUI includes a range defining visual element, corresponding to a data value, that is moveable via input received by the control GUI and defines two ranges of the data. The processor displays indicia associated with the range defining visual element indicating the corresponding data value. The processor receives input from the control GUI indicating the range defining visual element has been moved. In response, the processor visually emphasizes data in the data GUI having data values in a selected one of the defined data range.

Abstract: As disclosed herein, a method for visualizing a spread function corresponding to a data table includes receiving a set of user selected cells selected by a user that correspond to a data table, enabling the user to select a spread command from a set of available spread commands, executing the selected spread command on the set of user selected cells to provide a set of adjusted cells, displaying an interactive visualization to the user corresponding to the set of adjusted cells, and providing a data table corresponding to the set of adjusted cells. A computer program product and computer system corresponding to the method are also disclosed.

Abstract: An animated visualization of data on an electronic device having a limited display area (such as a mobile device with a touch screen input) is disclosed. The limited display area is partitioned into a thumbnail view of a full data set and an expanded view of a subset of the data that is highlighted within the thumbnail. The views are correspondingly animated such that the highlighted data appears to progressively slide across the thumbnail view such that the full set of data is ultimately displayed in the expanded view. The data displayed in the expanded view is kept in context by dynamically highlighting the corresponding data within the thumbnail view. Various actions are also disclosed, such as pausing/re-starting the animation, selectively pinning data and consolidating coupled data elements—in each case at any time during the animation.

Abstract: As disclosed herein, a method for visualizing a spread function corresponding to a data table includes receiving a set of user selected cells selected by a user that correspond to a data table, enabling the user to select a spread command from a set of available spread commands, executing the selected spread command on the set of user selected cells to provide a set of adjusted cells, displaying an interactive visualization to the user corresponding to the set of adjusted cells, and providing a data table corresponding to the set of adjusted cells. A computer program product and computer system corresponding to the method are also disclosed.

Abstract: A mechanism is provided in a data processing system for statistical clustering inferred from natural language to drive relevant analysis. The mechanism receives a natural language text from a user and processes the natural language text to identify an entity of interest and a focus of statistical analysis. The mechanism performs a follow-up question and answer conversation with the user to receiving from the user one or more driving factor values for the one or more driving factors. The mechanism determines at least one cluster of entities matching the One or more driving factor values and generates at least one data visualization of the data in the corpus for the focus of statistical analysis having a scope that is narrowed based on the at least one cluster of entities matching the one or more driving factor values.

Abstract: A method, a computer program product, and an information handling system is provided for identifying a causal relationship between metrics performing steps. The steps include receiving a correlation significance Sij between a metric Mi and a metric Mj; receiving a mutability attribute Ai for the Mi and a mutability attribute Aj for the metric Mj from a data source; and identifying the metric Mi causing the metric Mj with the Sij if only if the Ai is immutable and the Aj is mutable.

Abstract: A method, system and computer program product for evaluating the interestingness of correlated data. The fields of a dataset are classified by tagging the fields in terms of real world concepts. A correlation analysis on the dataset is performed to generate a correlation coefficient for each pair of fields of correlated data items. An “ontological distance” between the tagged concepts for each pair of fields of correlated data items represented as nodes in the ontology is determined. A score is generated indicating an interestingness of correlation for each pair of fields of correlated data items based on the correlation coefficient and the ontological distance between the tagged concepts for each pair of fields of correlated data items. By utilizing the ontological distance with the correlation analysis to determine the interestingness of correlation, correlations that may not be obvious to users and unexpectedly correlated may be identified.

Abstract: A method, system and computer program product for evaluating the interestingness of correlated data. The fields of a dataset are classified by tagging the fields in terms of real world concepts. A correlation analysis on the dataset is performed to generate a correlation coefficient for each pair of fields of correlated data items. An “ontological distance” between the tagged concepts for each pair of fields of correlated data items represented as nodes in the ontology is determined. A score is generated indicating an interestingness of correlation for each pair of fields of correlated data items based on the correlation coefficient and the ontological distance between the tagged concepts for each pair of fields of correlated data items. By utilizing the ontological distance with the correlation analysis to determine the interestingness of correlation, correlations that may not be obvious to users and unexpectedly correlated may be identified.

Abstract: Display data in a data graphical user interface (GUI) on a display device and display, by a processor, a control GUI on the display device representing the data. The control GUI includes a range defining visual element, corresponding to a data value, that is moveable via input received by the control GUI and defines two ranges of the data. The processor displays indicia associated with the range defining visual element indicating the corresponding data value. The processor receives input from the control GUI indicating the range defining visual element has been moved. In response, the processor visually emphasizes data in the data GUI having data values in a selected one of the defined data range.