Abstract: 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.

Abstract: 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.

Abstract: Embodiments relate to a system, product, and method for visually presenting data based on a viewing and change history. A first exploration is created, including a first and second view of a first data version. A second exploration is created, including a third and fourth view of a second data version. A first combined view of the first and second views is created containing a first data visualization of the first data version. A second combined view of the third and fourth views is created containing a second data visualization of the second data version. The first and second combined views are compared to display the structural changes between the first and second data versions.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments relate to visually encoding data and analyzing an associated dataset. More specifically, the embodiments relate to encoding a dynamic dataset and supporting data exploration of the dynamic dataset. In various embodiments, data and data viewing history are tracked according to defined criteria, which form a data version and viewing analysis record. The data and record can be displayed in many ways. In one embodiment, a visual display of differences between a first version and a second version of data is shown. In another embodiment, the visual display is dynamic and changes in real-time.

Abstract: Embodiments relate to visually encoding data and analyzing an associated dataset. More specifically, the embodiments relate to encoding a dynamic dataset and supporting data exploration of the dynamic dataset. In various embodiments, data and data viewing history are tracked according to defined criterion, which form a data version and viewing analysis record. The data and record can be displayed in many ways. In one embodiment, a visual display of differences between a first version and a second version of data is shown. In another embodiment, the visual display is dynamic and changes in real-time.

Abstract: Embodiments relate to a system, product, and method for visually presenting data based on a viewing and change history. A first exploration is created, including a first and second view of a first data version. A second exploration is created, including a third and fourth view of a second data version. A first combined view of the first and second views is created containing a first data visualization of the first data version. A second combined view of the third and fourth views is created containing a second data visualization of the second data version. The first and second combined views are compared to display the structural changes between the first and second data versions.

Abstract: Embodiments relate to visually encoding data and analyzing an associated dataset. More specifically, the embodiments relate to encoding a dynamic dataset and supporting data exploration of the dynamic dataset. In various embodiments, data and data viewing history are tracked according to defined criterion, which form a data version and viewing analysis record. The data and record can be displayed in many ways. In one embodiment, a visual display of differences between a first version and a second version of data is shown. In another embodiment, the visual display is dynamic and changes in real-time.

Abstract: Embodiments relate to visually encoding data and analyzing an associated dataset. More specifically, the embodiments relate to encoding a dynamic dataset and supporting data exploration of the dynamic dataset. In various embodiments, data and data viewing history are tracked according to defined criterion, which form a data version and viewing analysis record. The data and record can be displayed in many ways. In one embodiment, a visual display of differences between a first version and a second version of data is shown. In another embodiment, the visual display is dynamic and changes in real-time.

Abstract: 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.

Abstract: Embodiments relate to visually encoding data and analyzing an associated dataset. More specifically, the embodiments relate to encoding a dynamic dataset and supporting data exploration of the dynamic dataset. In various embodiments, data and data viewing history are tracked according to defined criterion, which form a data version and viewing analysis record. The data and record can be displayed in many ways. In one embodiment, a visual display of differences between a first version and a second version of data is shown. In another embodiment, the visual display is dynamic and changes in real-time.

Abstract: Embodiments relate to visually encoding data and analyzing an associated dataset. More specifically, the embodiments relate to encoding a dynamic dataset and supporting data exploration of the dynamic dataset. In various embodiments, data and data viewing history are tracked according to defined criterion, which form a data version and viewing analysis record. The data and record can be displayed in many ways. In one embodiment, a visual display of differences between a first version and a second version of data is shown. In another embodiment, the visual display is dynamic and changes in real-time.

Abstract: Embodiments relate to visually encoding data and analyzing an associated dataset. More specifically, the embodiments relate to encoding a dynamic dataset and supporting data exploration of the dynamic dataset. In various embodiments, data and data viewing history are tracked according to defined criteria, which form a data version and viewing analysis record. The data and record can be displayed in many ways. In one embodiment, a visual display of differences between a first version and a second version of data is shown. In another embodiment, the visual display is dynamic and changes in real-time.

Abstract: Embodiments relate to visually encoding data and analyzing an associated dataset. More specifically, the embodiments relate to encoding a dynamic dataset and supporting data exploration of the dynamic dataset. In various embodiments, data and data viewing history are tracked according to defined criterion, which form a data version and viewing analysis record. The data and record can be displayed in many ways. In one embodiment, a visual display of differences between a first version and a second version of data is shown. In another embodiment, the visual display is dynamic and changes in real-time.

Abstract: 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.

Abstract: 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.