Abstract: A method, system, and/or computer program product modify a hardware device based on a time series of data. One or more processors standardize a time series of data received from sensors that are monitoring a hardware device. The processor(s) establish time ranges before, during and after each event. The processor(s) determine which events represented by the time series of data are significant by comparing means and trends of time sub-series corresponding to the time ranges before, during, and after each event, and then generate a modified time series of data by reducing a number of significant events described by the time series of data, which is used to modify the hardware device.

Abstract: A computer-implemented method modifies a hardware device based on a time series of data. One or more processors standardize a time series of data received from sensors that are monitoring a hardware device. The processor(s) determine a time delta that measures how long a disruption in the time series lingers after an event that is detected by the sensors, and use the time delta to establish time ranges before, during and after each event. The processor(s) determine which events represented by the time series of data are significant, and then reduce a number of significant events described by the time series of data to a predefined level by removing events that have tags not found associated with other events in the time series of data to generate a modified time series of data, which is used to modify the hardware device.

Abstract: A method, system, and/or computer program product modify a hardware device based on a time series of data. One or more processors standardize a time series of data received from sensors that are monitoring a hardware device. The processor(s) determine a time delta that measures how long a disruption in the time series lingers after an event that is detected by the sensors, and use the time delta to establish time ranges before, during and after each event. The processor(s) determine which events represented by the time series of data are significant, and then reduce a number of significant events described by the time series of data to a predefined level by removing events that have tags not found associated with other events in the time series of data to generate a modified time series of data, which is used to modify the hardware device.

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 computer-implemented method modifies a hardware device based on a time series of data. One or more processors standardize a time series of data received from sensors that are monitoring a hardware device. The processor(s) determine a time delta that measures how long a disruption in the time series lingers after an event that is detected by the sensors, and use the time delta to establish time ranges before, during and after each event. The processor(s) determine which events represented by the time series of data are significant, and then reduce a number of significant events described by the time series of data to a predefined level by removing events that have tags not found associated with other events in the time series of data to generate a modified time series of data, which is used to modify the hardware device.

Abstract: A method, system, and/or computer program product modify a hardware device based on a time series of data. One or more processors standardize a time series of data received from sensors that are monitoring a hardware device. The processor(s) determine a time delta that measures how long a disruption in the time series lingers after an event that is detected by the sensors, and use the time delta to establish time ranges before, during and after each event. The processor(s) determine which events represented by the time series of data are significant, and then reduce a number of significant events described by the time series of data to a predefined level by removing events that have tags not found associated with other events in the time series of data to generate a modified time series of data, which is used to modify the hardware device.

Abstract: According to one embodiment of the present invention, a computer-implemented method comprises generating a plurality of charts to visually represent a multivariate data set, mapping variables within the multivariate data set to components of each chart, calculating a score value for each chart based on a plurality of factors, and presenting one or more of the plurality of charts based on corresponding score value.

Abstract: A data portion of a data set utilized in a computerized visualization is analyzed to identify one or more areas of interest each including data values representing distinguishable features relative to the data set. An explanation for the data values of each of the one or more areas of interest is determined. Each explanation is based on other data portions of the data set contributing to the distinguishable features. At least one display layer including labels describing the one or more areas of interest is generated. The labels include the explanation for each of the one or more areas of interest. The at least one display layer is disposed over the computerized visualization to produce an annotated visualization with the labels positioned proximate the one or more areas of interest.

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 of the present invention, a system generates an alternative visualization of a data set based on a specification of a selected first visualization of the data set and parameters comprising information about a data visualization goal and statistical relations between two of more variables in the data set. Embodiments of the present invention further include a computer program product for generating an alternative visualization in substantially the same manners described above.

Abstract: According to one embodiment of the present invention, a computer-implemented method generates an alternative visualization of a data set based on a specification of a selected first visualization of the data set and parameters comprising information about a data visualization goal and statistical relations between two or more variables in the data set. Embodiments of the present invention further include a system and computer program product for generating an alternative visualization in substantially the same manners described above.

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: Techniques are described for genomically defining digital genes encoding data visualization elements and potential incremental changes to the elements as the basis for a genetic selection process for automated generating of data visualizations. In one aspect, a method includes receiving set of input data. The method further includes generating digital genes that genomically define data visualization elements based on the input data, and that define potential incremental changes to the data visualization elements. The method further includes executing a genetic selection process with respect to one or more fitness functions on populations of candidate data visualizations that are based on the genomically defined data visualization elements. The method further includes outputting final data visualization output generated by the genetic selection process.

Abstract: Techniques are described for genomically defining digital genes encoding data visualization elements and potential incremental changes to the elements as the basis for a genetic selection process for automated generating of data visualizations. In one aspect, a method includes receiving set of input data. The method further includes generating digital genes that genomically define data visualization elements based on the input data, and that define potential incremental changes to the data visualization elements. The method further includes executing a genetic selection process with respect to one or more fitness functions on populations of candidate data visualizations that are based on the genomically defined data visualization elements. The method further includes outputting final data visualization output generated by the genetic selection process.

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 computing device includes at least one processor, and at least one module operable by the at least one processor to receive data representing a hierarchy, wherein the hierarchy comprises at least one set of sibling nodes and a respective parent node, generate a condensed hierarchy by determining a grouping for the at least one set of sibling nodes, determine whether the at least one set of sibling nodes can be represented by the respective parent node, based at least in part on the grouping for the at least one set of sibling nodes, and responsive to determining that the at least one set of sibling nodes can be represented by the respective parent node, remove the at least one set of sibling nodes from the condensed hierarchy. The at least one module may further be operable by the at least one processor to output the condensed hierarchy for display.

Abstract: Techniques are described for a real-time visualization of data in an integrated authoring environment. In one example, a method includes receiving text information. The method also includes comparing the dynamically entered text information against a data source, wherein the comparing comprises applying natural language processing (NLP) to determine one or more assertion statements in the text information that references data from the data source. The method also includes retrieving the referenced data associated with the one or more assertion statements from the data source. The method further includes generating a visual representation of the referenced data associated with the one or more assertion statements. The method also includes displaying the visual representation, wherein the visual representation is integrated with an authoring interface.

Abstract: A method for determining user preferences for data visualizations is provided. The method may include receiving data visualizations. The method may also include collecting the shapes, the line segments, and the colors associated with the data visualizations. The method may further include converting the shapes and the line segments to polygonal outlines. Additionally, the method may include categorizing and measuring the line segments. The method may further include identifying and categorizing the angles formed by the line segments and determining weighted values for the angles. The method may further include calculating the total length for the line segments, and the total weighted value for the angles. The method may also include characterizing the line segments based on the categorization of the line segments and the angles based on the categorization of the angles. The method may further include scoring the at least one data visualization based on the characterizations.

Abstract: A method for determining user preferences for data visualizations is provided. The method may include receiving data visualizations. The method may also include collecting the shapes, the line segments, and the colors associated with the data visualizations. The method may further include converting the shapes and the line segments to polygonal outlines. Additionally, the method may include categorizing and measuring the line segments. The method may further include identifying and categorizing the angles formed by the line segments and determining weighted values for the angles. The method may further include calculating the total length for the line segments, and the total weighted value for the angles. The method may also include characterizing the line segments based on the categorization of the line segments and the angles based on the categorization of the angles. The method may further include scoring the at least one data visualization based on the characterizations.

Abstract: 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.