International Business Machines Corporation

Vinay N Wadhwa

Graham A Watts

Alireza Pourshahid

Qing Wei

Graham John Wills

Valentin Balteanu

Jason Hiltz-Laforge

Stephen David Gibson

Michael Iles

Michael A Duplessis

Andreas Eminidis

A computer-implemented method, a computer program product, and a computer system for determining and displaying connected insights based on a connection between data visualizations. A computer detects the second data visualization created on a coherent exploration path with the first data visualization. The computer determines, from first insights in first date frame for the first data visualization, top first insights that are interesting to the second data visualization. The computer determines, for respective ones of the top first insights, calculated fields representing states of data. The computer adds the calculated fields to a query to obtain a second data frame for the second data visualization. The computer determines second insights in the second data frame, where the second insights include data facts drawn from the connection between the first data visualization and the second data visualization. The computer displays the second insights along with the second data visualization.

Connected insights in a business intelligence application

A processor may receive information from a user. The information may include one or more datums. The processor may analyze the information for one or more correlations between the one or more datums. The processor may translate each correlation into a word group. Each word group may be associated with an icon. The processor may rank each word group based on a predictive importance. The processor may generate a set of visualizations based on the ranked word groups. The ranked word groups may each be associated with contextual metadata that enables generation of predictive actions.

Automatic visualization and inquiry generation

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.

Using ontology for cause-effect analysis

Applying visual treatments to business intelligence dashboards is provided. A user comment in a dashboard of a business intelligence tool is analyzed. A context of the user comment is identified. A visual treatment is applied to a data visualization on the dashboard based on the context of the user comment.

Smart anchoring of visual treatments corresponding to user comments in business intelligence visualizations

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.

Using ontological distance to measure unexpectedness of correlation

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.

Cross-platform document presentation

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.

Data model augmentation

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.

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Ottawa, Canada

Vinay N Wadhwa