International Business Machines Corporation

Qliktech International Ab

Mohsen Rais-Ghasem

Graham A Watts

Qing Wei

Robin Neil Grosset

Ralf Vierich

Michael E Styles

Elif Tutuk

Robert Y Nonez

Claudiu D Popescu

Christine K Fulford

Akash Patel

Steven Pressland

Boris Dachevski

In an aspect, provided is a method comprising monitoring one or more data analysis sessions, determining, based on the monitoring, a common data analysis technique performed across common data analysis sessions, identifying the common data analysis technique as a precedent, and providing the precedent to a precedent engine.

Cognitive rule engine

Set analysis may be used to determine the best data analysis model(s) (e.g., data charts, data tables, data graphs, data maps, graphical objects, key performance indicators (KPIs), etc.) for representing the results of an undetermined query (e.g., an imprecise query, an undefined query, an incomplete query, a partially expressed query, a portioned query, etc.).

Methods and systems for undetermined query analytics

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

Techniques are described for generating a hierarchical group based on a set of data. In one example, a method includes classifying two or more data items from a set of data with respect to a library of ontological concepts. The method further includes classifying the two or more data items with respect to lexical correlations between the two or more data items. The method further includes generating a hierarchical group in which the two or more data items are partitioned into one or more hierarchical partitions based at least in part on the classifying with respect to the library of ontological concepts and the classifying with respect to the lexical correlations, wherein each of the one or more hierarchical partitions comprises the two or more data items.

Generating partitioned hierarchical groups based on data sets for business intelligence data models

A computing device may receive a natural language query. The computing device may determine, based on the natural language query, a set of search criteria. The computing device may determine, from a plurality of data sets, two or more data sets that are relevant to the natural language query based at least in part on matches between the set of search criteria and attributes of the plurality of data sets. The computing device may join the two or more data sets, including joining one or more rows and one or more columns of the two or more data sets to produce a joint data set. The computing device may output a representation of the joint data set.

Search-based detection, link, and acquisition of data

A computing device receives a natural language query. The computing device determines, based on the natural language query, a set of search criteria. The computing device determines, from a plurality of data sets, two or more data sets that are relevant to the natural language query based at least in part on matches between the set of search criteria and attributes of the plurality of data sets. The computing device joins the two or more data sets, including joining one or more rows and one or more columns of the two or more data sets to produce a joint data set. The computing device outputs a representation of the joint data set.

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

Provided are a computer implemented method, computer program product, and system for generating a combined report. One or more queries are constructed using a Business Intelligence (BI) metadata model and one or more query declarations. The one or more queries are executed on a semantic model to obtain one or more semantic result sets. One or more data source result sets are received from one or more data sources. The one or more semantic result sets and the one or more data source result sets are combined to construct one or more combined result sets. The one or more combined result sets are rendered to form a combined report.

Enabling business intelligence applications to query semantic models

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.

One or more processors sort a plurality of geometric shapes using one or more size criteria. One or more processors determine whether the plurality of geometric shapes include at least one geometric shape that encapsulates or partially overlaps at least one other geometric shape. One or more processors assign a hierarchical order for the plurality of geometric shapes based, at least in part, on the one or more size criteria and one or more criteria of one or both encapsulation and partial overlap.

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Ottawa, Canada

Mohsen Rais-Ghasem