Abstract: 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.).

Abstract: Methods and systems are disclosed for generating and using a knowledge graph. In an aspect, the knowledge graph may be generated based on data fields for one or more datasets associated with one or more parameters extracted from a group of chart data structures. In another aspect, a query dataset may be analyzed, and one or more query data fields may be extracted from the query dataset. The one or more query data fields may be compared to a knowledge graph stored in a graph database to determine one or more result data fields. A context may be determined for each of the one or more result data fields, and an associated data set may be determined. Based on the context, each of the associated data sets may be scored, and a recommended analysis may be presented to a user.

Abstract: 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.).

Abstract: 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.

Abstract: 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.

Abstract: 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.).

Abstract: 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.

Abstract: Methods and systems are disclosed for generating and using a knowledge graph. In an aspect, the knowledge graph may be generated based on data fields for one or more datasets associated with one or more parameters extracted from a group of chart data structures. In another aspect, a query dataset may be analyzed, and one or more query data fields may be extracted from the query dataset. The one or more query data fields may be compared to a knowledge graph stored in a graph database to determine one or more result data fields. A context may be determined for each of the one or more result data fields, and an associated data set may be determined. Based on the context, each of the associated data sets may be scored, and a recommended analysis may be presented to a user.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods may automate management of natural language queries of enterprise data. In one example, a method includes performing natural language processing and semantic processing on a natural language query to identify data sets relevant to the natural language query. The method further includes grouping the data sets into one or more query domains based at least in part on one or more relationships among the data sets. The method further includes prioritizing the query domain sets. The method further includes loading one or more of the query domain sets in an order based on the prioritizing of the query domain sets.

Abstract: 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.