Abstract: 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.

Abstract: 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.

Abstract: A computer-implemented method, a computer program product, and a computer system for identifying an influencer combination having a root cause to a key performance indicator change. The computer system analyzes metadata to discover semantic information for each column of data, identifies candidate factors that categorize a target performance indicators (KPI), groups the candidate factors into groups based on hierarchies which are included in the semantic information. For respective ones of the hierarchies, the computer system chooses most influential levels as influencer candidates. The computer system creates a stratified sample based on a distribution of target KPI values and evaluates an influential strength to the target KPI. The computer system identifies top influencers in the influencer candidates, based on influential strengths of respective ones of the influencer candidates.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method, apparatus, system, and computer program product for creating a forecasting model for time series data. Seasonality is removed from the times series data having the seasonality and trends to form deseasoned time series data. Trend models are created for sets of candidate change points in the deseasoned time series data. The trend models are for the sets of candidate change points without the seasonality. Seasonal models are created using the time series data without the trends. The seasonal models have different time periods for the seasonality. The trend models are combined with the seasonal models to form complete models that take into account the seasonality and the trends. The forecasting model is selected from the complete models in which the forecasting model is a best fit to a set of criteria.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.