International Business Machines Corporation

Qing Wei

Graham A Watts

Mohsen Rais-Ghasem

Alireza Pourshahid

Vinay N Wadhwa

Robin Neil Grosset

Stanley L Chauvin

Roch Lefebvre

Stewart James Winter

Murray J Reid

James D Miller-Cushon

Emilie Noel

Embodiments of the present invention provide systems and methods for visualizing and querying data. Data subsets within an original table are examined. Values within the data subsets are extracted. Based on the extracted values, a new table is generated. The data contents of the original table differ from the data contents of the new table. Thus, the resulting visualization of the original table differs from the resulting visualization of the new table.

Querying and projecting values within sets in a table dataset

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

A system and method for assigning concepts to sets of values. A dataset is selected, wherein the dataset includes a plurality of records, each record including a plurality of named fields, wherein each record includes each of the named fields. Each named field is assigned to a category and a concept is selected by identifying one or more named fields that, when combined, provide a unique value for each record. In some embodiments, the data is organized as a table, wherein the records are rows and the named fields are columns.

Detection and creation of appropriate row concept during automated model generation

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

Techniques are described for modeling information from a data source. In one example, a method for modeling information from a data source includes comparing, with one or more computing devices, a data item heading from the data source with concept keywords in a concept library, the concept library comprising a plurality of concepts and one or more of the concept keywords in at least one language associated with each of one or more of the concepts. The method further includes identifying, with one or more computing devices, one or more matches between the data item heading and one or more concept keywords associated with a particular concept from among the concepts comprised in the concept library. The method further includes identifying, with one or more computing devices, the data item heading as being associated with the particular concept.

Business intelligence data models with concept identification using language-specific clues

A system and method for assigning concepts to sets of values. Data is received, wherein the data is organized as a plurality of named fields and as two or more sets of values associated with the named fields, wherein each named field is assigned to a category. For each category, determine whether there is at least one identifier field for that category, wherein each identifier field is a named field that acts as an identifier for that category, and identify identifier fields, if any, for each category that have a unique value in the identifier field for that category for each set of values. Then select one of the categories as a concept representing the sets of values. In some embodiments, the data is organized as a table, wherein the named fields are columns and the sets of values are rows.

Provided are techniques for presenting a data visualization on an off-line mobile device. A report specification is parsed to detect a drill definition to navigate from a report viewer to an interactive visualizer. Data package report specifications are generated to create data and metadata. Data package report output is obtained for each of the data package report specifications. Data and metadata in each data package report output are converted. The converted data and metadata are stored into a data package. A mobile content package is created using the data package and a visualization specification. A report and the mobile content package are delivered to the off-line mobile device. The report viewer is used to present the report. In response to a user interacting with a user interface control on the presented report, the interactive visualizer is launched to present the data visualization using the mobile content package.

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Ottawa, Canada

Qing Wei