International Business Machines Corporation

Craig A Statchuk

Peter Bak

Avi Yaeli

Gilad Mordechai Saadoun

Harold Jeffrey Ship

Changying Sun

Marius Cosma

Steven R McDougall

Alexandru Pulbere

Yandu S Oppacher

Neil E Bartlett

Min Li

Zhong Tian

John V Meegan

Michael Iles

Mario Daigle

Christopher B Ferris

Xiao Feng Zhao

Carl Randall Chafy

Wayne Robert Stevens

Michael G Rowling

Ronald L Gagnier

David J Ridgeway

Michael A Iles

Steven R Mcdougall

A mechanism is provide for preparing a high-quality data repository. Data and related metadata from a set of data sources are ingested thereby forming a set of unprepared data. The set of unprepared data is transformed based on a set of functions into a set of transformed data. A set of semantic text descriptions that detail the transformation of the set of unprepared data to the set of transformed data is generated using a first set of semantic associations, a second set of semantic associations, and a set of semantic transformation associations. The set of transformed data is tested against one or more governance policies that tracks data lineage to ultimately show that prepared data is in compliance. Responsive to the set of transformed data adhering to the one or more governance policies, a high-quality data repository is automatically built using the transformed data.

Preparing high-quality data repositories sets utilizing heuristic data analysis

Techniques are described for creating geospatial elements based on geographical data. In one example, a method for creating geospatial elements based on geographical data includes collecting one or more geographical indicators comprised in a set of operational data, the geographical indicators indicating one or more geographical areas. The method further includes collecting, from a set of geographical data, geospatial data associated with the one or more geographical areas indicated by the one or more geographical indicators. The method further includes generating one or more aggregated geospatial elements based on the one or more geographical indicators and the geospatial data.

Generating business intelligence geospatial elements

A mechanism is provided for preparing a high-quality data repository. Data and related metadata from a set of data sources are ingested thereby forming a set of unprepared data. The set of unprepared data is transformed based on a set of functions into a set of transformed data. A set of semantic text descriptions that detail the transformation of the set of unprepared data to the set of transformed data is generated using a first set of semantic associations, a second set of semantic associations, and a set of semantic transformation associations. The set of transformed data is tested against one or more governance policies that tracks data lineage to ultimately show that prepared data is in compliance. Responsive to the set of transformed data adhering to the one or more governance policies, a high-quality data repository is automatically built using the transformed data.

A mechanism is provided for advanced search-term disambiguation. Responsive to detecting a search term being entered into an autocomplete search field of a search engine, a determination is made of a set of terms from a storage device upon which a search is to be performed. For each term in the set of terms, a determination is made of a semantic distance and independence (S) score to each term candidate in a set of term candidates. The results of the semantic distance and independence (S) scores are ranked and then pruned down to a predetermined number of autocomplete results. A subset of term candidates associated with the predetermined number of autocomplete results from the set of term candidates is then presented to a user as autocomplete suggestions to the search.

Advanced search-term disambiguation

A method, system and computer program product for building a data query engine. Initial taxonomies that describe and categorize data are built by expert users (e.g., data scientists) employing machine learning algorithms. The data is also indexed and stored in an index. Queries are then received from non-expert users to query the data based on data categorization from built taxonomies and the indexing. After the queries are executed using the machine learning algorithms in an environment (e.g., Hadoop&#xae;), the results of the queries are rated for relevance, precision and accuracy. The machine learning algorithms are also rated based on the number of successful queries. Those machine learning algorithms with a rating above a threshold are identified to be utilized to scan new data to be stored in the index to provide a new environment that replaces the initial environment.

Building a data query engine that leverages expert data preparation operations

A system for joining modeled data with new data sources. Modeled tabular data containing categorical and metric data is received. New data containing structured or non-structured data is received. The new data is indexed and data clusters and/or data facets are identified. Data clusters and data facets are also identified within the received modeled data. A textual query from a user is received and semantically indexed into keywords. Data clusters and data facets within the new data are matched to the keywords in the indexed query. Data clusters and data facets within in the modeled data are matched to the keywords in the indexed query. A table is generated containing the modeled data and matching data clusters and data facets from the new data.

Database joins using uncertain criteria

A computer program product, a method, and an information handling system is provided to track and retrieve licensing agreements of features integrated into an application by detecting during development of an application installation of one or more features having a licensing agreement associated with using the one or more features; storing in a database a mapping between the one more installed features to a corresponding licensing agreement for each of the one or more installed features; and providing support to retrieve for the each installed features, the corresponding licensing agreement.

Embedded integrated component governance policy

Embodiments of the present invention disclose a method, computer program product, and system for optimizing spatial queries, the method comprising a computer receiving a spatial data set, and a target spatial shape. The computer determining a distribution data set which aligns with the geometry of the spatial data set. The computer performing a query to determine a list of full and partial shapes of the spatial data set located within the target spatial shape. The computer determining a ratio for a determined partial shape of the spatial data set using the distribution data set. The computer determining an apportioned value of a variable of the spatial data set within the determined partial shape. The computer summarizing into a result set the value of the variable within the determined list of full and partial shapes of the spatial data set.

Geospatial queries using associated distribution data

A method and system for generating a map using a computer is based on data and weighted factors to minimize corresponding projection distortions. The method and system includes determining visualization goals from analyzing a set of datasets for a map using the computer. A set of visualization characteristics are calculated for each dataset based on the visualization goals using the computer. The visualization characteristics are analyzed to weight factors for each of the datasets. Each of the weighted factors is adjusted based on the relevance of each of the datasets for visualization of the map. An aggregate vector of weighted factors is calculated based on all of the datasets, and the map for visualization is generated based on the aggregate vector of weighted factors.

Method to optimize the visualization of a map's projection based on data and tasks

A mechanism is provided for automatically performing join operations. Source data is received and a metadata model is received. The metadata model includes a hierarchical structure. The source data is aligned to the hierarchical structure in the metadata model to form a source data hierarchy. Based on the source data hierarchy, the source data is joined to geocoded information.

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Kars, Canada

Craig A Statchuk