International Business Machines Corporation

Lucent Technologies Inc.

Graham John Wills

Daniel Jay Rope

T Alan Keahey

Lin Luo

Stephen Gregory Eick

Changying Sun

Mohammed Mostafa

Jing-Yun Shyr

Graham Derek Wallis

Thomas J Ball

Stephen David Gibson

Vinay N Wadhwa

Randy L Hackbarth

Thomas Alan Keahey

James David Herbsleb

David L Atkins

Valentin Balteanu

Daniel V Walker

Jing James Xu

Damir Spisic

Fan Li

David Gray Boyer

Sier Han

Yea Jane Chu

Rebecca Elizabeth Grinter

Peter Andrew Mataga

Lalita Jategaonkar Jagadeesan

Franciscus J J Van Ham

Peter R Broadbent

Behrooz Ashtiani

Marc S Altshuller

Leo A Coolen

Paul Jay Lucas

James Owen Coplien

Devendra G Tasgaonkar

A set of transition characteristics can be identified. The set of transition characteristics can include continuities and discontinuities between data fields and data visualization channels among a plurality of data visualizations. A transition tuple for each of a plurality of pairs of the data visualizations can be determined and mapped to a matrix. The matrix can be transposed to generate vectors, each vector representing a type of transition characteristic, at least one of the vectors comprising a plurality of elements equaling a total number of the determined transition tuples. The distribution of the data fields and the data visualization channels across the data visualizations can be determined by performing statistical analysis on each of the vectors. A collection of the data visualizations can be determined based on the statistical analysis performed on each of the vectors, the collection of the data visualizations can include a subset of the data visualizations.

Determining a collection of data visualizations

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 computer-implemented method, system and computer program product for providing predictions tailored for a specific domain. Each field within a set of datasets is classified with a concept defined in a domain specific ontology. After receiving a target field and identifying a target concept tagged to the target field, concepts that have an influential impact directly to the target concept or indirectly to a concept tagged to a field associated with the target field are identified. Fields are then identified as candidates for predictors from the identified concepts using a semantic relationship and a numeric association combined approach. An influential confidence score and a numeric analytics score are then calculated for each of these candidates upon which a final confidence score is computed. The identified candidates are ranked based on the final confidence scores, and the top ranked candidates for predictors are selected to build a statistical model.

Providing predictive analytics with predictions tailored for a specific domain

According to one embodiment, a method for generating a plurality of candidate visualizations. The method may include receiving a scenario description. The method may also include collecting a plurality of expert data using a training system based on the received scenario description. The method may further include generating at least one predictive model based on the collected plurality of expert data in order to execute the at least one generated predictive model during an application of a plurality of genetic algorithms.

Candidate visualization techniques for use with genetic algorithms

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 computer-implemented method, system, and computer program product for generating a semantic model of data comprising a plurality of data items. Semantic concepts are identified for each data item in the plurality of data items. Data concepts are identified for each data item in the plurality of data items based on data characteristics of the data item and using a data ontology that defines the data concepts that are used to represent the data characteristics. The semantic model of the data is generated using the semantic concepts and the data concepts identified for the plurality of data items.

Holistic knowledge representation for semantic modeling of structured data

A computer-implemented method for joining data sets with mismatched geographic location naming conventions is provided. The method includes identifying, by the computer, a first dataset and a second dataset as join candidates. The method also includes joining, by the computer, the first dataset and the second dataset when, each row of the first user dataset is associated with a single geographic identifier using a geographic knowledge dataset that includes a geographic name lookup table and each row of the second user dataset is associated with a single geographic identifier using the geographic knowledge dataset, wherein the geographic name lookup table includes a plurality of alias names for each of a plurality of unique geographic locations.

Method to automatically join datasets with different geographic location naming conventions

A data visualization tool ('tool') configured to generate a visualization for tree data. The tool can calculate a number of hierarchical levels of the tree data. The tool can further subdivide a presentation area for the data visualization into a number of concentric parts. The concentric parts are equivalent to the number of hierarchical levels. Each of the concentric parts corresponds to one of the hierarchical levels. Further, the tool can graphically generate the data visualization for presentation via an electronic display. A first of the concentric parts that corresponds to a highest of the hierarchal levels is an outermost one of the concentric parts in the presentation area. Each successive concentric part inward corresponds to a next lower hierarchical level.

Generating an outside-in hierarchical tree visualization

A big data analytics system includes an event monitoring controller that detects a user targeted event from an initial digital query data flow. An event conversion controller determines at least one contextual element associated with the at least one user targeted event, and automatically converts the at least one contextual element into a search criteria. An external data query builder controller automatically executes an external search query based on the search criteria. A data fusing controller automatically fuses the at least one contextual element generated from the at least one user targeted event, together with external search results generated from the external search query and the initial query data flow to generate a fused data file.

Automatically connecting external data to business analytics process

For each vertex of a network graph that includes a set of vertices, a meta-property of the vertex is determined relative to other vertices of the set of vertices, a distance of the vertex from a user's viewpoint of a three-dimensional (3D) rendering of the network graph is determined, and a ranking figure is calculated as a function of the determined meta-property in combination with the determined distance. The set of vertices of the network graph is ranked as a function of the respective calculated ranking figures. Labels for a subset of the set of vertices are selectively displayed on a display as a function of the ranking.

Adaptive labeling of network graphs

A computer-implemented method, system, and computer program product for classifying a concept of a data item. A data item label for the data item is received. The data item label is analyzed using a natural language processing tool to generate additional lexical information for the data item label. A search query is built using the additional lexical information for the data item label. The search query is used to search a clue concept index to produce a search result. The clue concept index comprises clue concept records that identify clues for concepts. The search result identifies clue concept records from the clue concept index that match the search query. A concept is selected from the clue concept records identified in the search result as the concept for the data item.

Disambiguation of concept classifications using language-specific clues

System, method, and computer program product for measuring transitions between visualizations, the method comprising identifying data fields represented in a first visualization and one or more presentation characteristics for the data fields represented in the first visualization, identifying data fields represented in a second visualization and one or more presentation characteristics for the data fields represented in the second visualization, determining a plurality of transition scores, wherein each transition score represents a difference or similarity between the first and second visualizations, relative to either the identified data fields or the presentation characteristics, and generating a composite measure of transition between the first and second visualizations from the plurality of transition scores.

Measuring transitions between visualizations

Performing an operation comprising analyzing an electronic text corpus by a natural language processing (NLP) algorithm executing on a processor to determine a set of target terms specified in the electronic text corpus, analyzing the electronic text corpus by the NLP algorithm to determine a weight for each pair of terms in the set of target terms, and storing an indication of each pair of terms and the respective weight in a lookup table in a computer memory for runtime analysis of an input text data.

Statistical preparation of data using semantic clustering

A set of transition characteristics can be identified. The set of transition characteristics can include continuities and discontinuities between data fields and data visualization channels among a plurality of data visualizations. The set of transition characteristics can be identified by analyzing the plurality of data visualizations and identifying similarities and differences among the data fields and the data visualization channels. A distribution of the data fields and the data visualization channels across the plurality of data visualizations can be determined. A collection of the data visualizations can be determined based on the distribution of the data fields and the data visualization channels across the plurality of data visualizations. The collection of the data visualizations can include at least a subset of the plurality of data visualizations.

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Lisle, IL, United States of America

Graham John Wills