Abstract: Automated weighting is performed that includes transforming a behavior of each respective dimension of multiple dimensions of a selected group of events to a respective weight, the respective weight determined based on a distribution of values of the respective dimension, and where the weight determined for a first of the plurality of dimensions is greater than the weight determined for a second of the plurality of dimensions. Similarity values are computed indicating similarities between further events and the selected group of events, the similarity values based on a combination of the weights and distances between the further events and the selected group of events. Cohorts of the further events are generated by performing multi-level ranking that comprises ranking groups of the further events based on the similarity values, and applying merging to the groups to produce merged groups. The cohorts are visualized in a graphical visualization.

Abstract: Visualization of a cohort for high-dimensional categorical data is disclosed. One example is a system including a display module to identify real-time selection of a query data element in an interactive visual representation of high-dimensional categorical data elements comprising a plurality of categorical components. A matrix generator generates a binary distance matrix with columns representing categorical components, and entries in a row indicative of a degree of similarity of respective categorical components of the selected query data element to a data element represented by the row, and determines a category weighting matrix by associating a weight with entries in each column of the binary distance matrix. An evaluator evaluates a weighted similarity score for a data element represented by a row of the category weighting matrix based on entries of the row. A selector iteratively and interactively selects, based on weighted similarity scores, a cohort of categorical data elements.

Abstract: A technique for visualizing topics includes depicting topic bubbles including pixels. In one example, selected topics are identified from records based on scoring candidate terms in the records according to a user-specified metric and a metric selected from among frequencies of occurrence of records pertaining to the respective candidate terms, and negativity of sentiment expressed with respect to the candidate terms in the records. A visualization is generated including bubbles representing topics, the bubbles including pixels representing corresponding records. A bubble has a shape dependent upon a number of records and a time interval represented by the bubble. Visual indicators are assigned to the pixels in a given bubble according to values of an attribute expressed in the corresponding records for the topic represented by the given bubble, resulting in the analysis of the selected topics being less time consuming and labor intensive.

Abstract: Automated weighting is performed that includes transforming a behavior of each respective dimension of multiple dimensions of a selected group of events to a respective weight, the respective weight determined based on a distribution of values of the respective dimension, and where the weight determined for a first of the plurality of dimensions is greater than the weight determined for a second of the plurality of dimensions. Similarity values are computed indicating similarities between further events and the selected group of events, the similarity values based on a combination of the weights and distances between the further events and the selected group of events. Cohorts of the further events are generated by performing multi-level ranking that comprises ranking groups of the further events based on the similarity values, and applying merging to the groups to produce merged groups. The cohorts are visualized in a graphical visualization.

Abstract: In an example, high-dimensional data is projected to a multi-dimensional space to differentiate clusters of the high-dimensional data. A user selection of at least two of the clusters may be received and a plurality of dissimilar dimensions may be extracted from the at least two clusters. In addition, a user selected of a dissimilar dimension from the plurality of extracted dissimilar dimensions may be received. In response to receipt of the user selection of the dissimilar dimension from the plurality of dissimilar dimensions, a plurality of correlated dimensions to the dissimilar dimension may be determined. In addition, the plurality of dissimilar dimensions and the plurality of correlated dimensions may be displayed.

Abstract: Non-overlapping visualization of data records of a scatter plot is provided by providing rows and columns in a visualization screen containing cells representing respective data records. The rows correspond to value ranges of a first attribute, and the columns correspond to value ranges of a second attribute. The value ranges are automatically generated for the rows and columns from data values of the data records.

Abstract: A user-selected group of data points is received. Weighted distances between further data points with the user-selected group of data points are computed, the weighted distances computed based on respective weights assigned to dimensions of data points. Density-based grouping of the further data points is performed based on the computed weighted distances, the density-based grouping producing cohorts of data points. A graphical visualization is generated including pixels representing the user-selected group of data points and the cohorts of data points. The graphical visualization provides a temporal-based visualized identification of the cohorts with the user selected group of data points.

Abstract: Visualization of a cohort for high-dimensional categorical data is disclosed. One example is a system including a display module to identify real-time selection of a query data element in an interactive visual representation of high-dimensional categorical data elements comprising a plurality of categorical components. A matrix generator generates a binary distance matrix with columns representing categorical components, and entries in a row indicative of a degree of similarity of respective categorical components of the selected query data element to a data element represented by the row, and determines a category weighting matrix by associating a weight with entries in each column of the binary distance matrix. An evaluator evaluates a weighted similarity score for a data element represented by a row of the category weighting matrix based on entries of the row. A selector iteratively and interactively selects, based on weighted similarity scores, a cohort of categorical data elements.

Abstract: Using a contingency calculation based on a number of events sharing a collection of values of plural attributes, a discriminative metric is computed representing a statistical significance of the events that share the collection of values of the plural attributes. A visualization is generated that includes cells representing respective events, the visualization including a region containing a subset of the cells corresponding to the collection of values of the plural attributes, and the visualization including a significance visual indicator associated with the region to indicate the statistical significance of the events sharing the collection of values of the plural attributes.

Abstract: Color cell-based data placement systems, methods, and computer-readable storage media that visualize large amounts of multidimensional data on an output by rearranging data objects to variably grant more output space to areas with high data density and less output space to areas with low data density, and to variably rearrange overlapping data objects based on a number of data objects already placed at a preferred cell position for the data object.

Abstract: A method of processing a stream of raw data from a plurality of distributed data producing devices includes reducing the raw data to a plurality of representative synopsis coefficients, organizing the synopsis coefficients into a data structure with at least three dimensions, including a time window dimension and an accuracy dimension. Responsive to a detected anomaly in the data structure, at least one of a predetermined autonomous action and an action directed by a user is performed.

Abstract: In an example, high-dimensional data is projected to a multi-dimensional space to differentiate clusters of the high-dimensional data. A user selection of at least two of the clusters may be received and a plurality of dissimilar dimensions may be extracted from the at least two clusters. In addition, a user selected of a dissimilar dimension from the plurality of extracted dissimilar dimensions may be received. In response to receipt of the user selection of the dissimilar dimension from the plurality of dissimilar dimensions, a plurality of correlated dimensions to the dissimilar dimension may be determined. In addition, the plurality of dissimilar dimensions and the plurality of correlated dimensions may be displayed.

Abstract: Visual structures are produced to represent corresponding motifs, where the motifs correspond to repeating patterns in an input data set, where sizes of the visual structures are based on values of a characteristic associated with the motifs, and where a first of the motifs is nested within a second of the motifs to represent a hierarchical relationship of the patterns represented by the first and second motifs. The visual structures of corresponding motifs are presented for display in a visualization screen, where the visualization screen depicts the data in the input data set with the visual structures overlapping parts of depicted data. Interactive input is received specifying modification of a region containing motifs, wherein the specified modification includes interactive distortion of the visual structures in the region to change a size of the visual structures in the region.

Abstract: Selected topics are identified from records based on scoring candidate terms in the records according to a user-specified metric and at least one further metric selected from among frequencies of occurrence of records pertaining to the respective candidate terms, and negativity of sentiment expressed with respect to the candidate terms in the records. A visualization is generated that includes bubbles representing the respective topics, the bubbles including pixels representing corresponding records, where a given one of the bubbles has a shape dependent upon a number of records represented by the given bubble and a time interval represented by the given bubble. Visual indicators are assigned to the pixels in the given bubble according to values of an attribute expressed in the corresponding records for the topic represented by the given bubble.

Abstract: According to an example, fluctuations of a metric for events are determined. The fluctuations are for multiple time intervals. A pixel-based visualization of the fluctuations is generated, and the pixels represent amounts of the fluctuations.

Abstract: A system causes display of a visual representation of a cell-based calendar having cells representing data records, where the cells have corresponding visual indicators corresponding to values of the respective data records. The cells are arranged in plural blocks corresponding to time intervals of the cell-based calendar. Event visual indicators transparently overlay cells in at least some of the plural blocks, where the event visual indicators represent events that occurred in time intervals corresponding to the at least some blocks. The events are separate from activities represented by the data records. Providing the event visual indicators transparently overlaid over the cells in the at least some blocks depict a relationship and relative impact between the data records and the events.

Abstract: A pattern of geocoded pixels is generated by accessing data point values, where each data point value includes an attribute value and coordinates of a geographic location. Each data point value corresponds to a geocoded pixel that is positioned on the pattern based on the coordinates of the data point value such some geocoded pixels overlap other geocoded pixels. Different levels of the pattern of geocoded pixels correspond to a different degree of overlap between the geocoded pixels. The different levels of the pattern of geocoded pixels are associated with different magnification levels of a geographic map such that changing a magnification level of the geographic map causes a degree of overlap between the geocoded pixels of the pattern to change.

Abstract: A value corresponding to an adjustable control element is received. Predicted data is computed from seasonal data, where the computing is according to applying preservation of peaks in the seasonal data and applying time distance biasing in which more recent data points in the seasonal data are weighted higher than less recent data points. Relative weighting of the peak preservation and the time distance biasing is based on the received value corresponding to the adjustable control element.

Abstract: A method, apparatus, and system are disclosed for providing content queries for multi-dimensional datasets. One embodiment is a method for software execution that displays, on an interactive graph, categories and values of a multi-dimensional dataset. A content query is issued from a user for a value from at least one of the categories. Interactive graphs are generated that are responsive to the content query.

Abstract: According to an example, in a method for displaying visual analytics of entity data, geographic locations of entities may be plotted as first pixel cells on a first region and as second pixel cells on a second region of a geographic map. A determination may be made that the first pixel cells have a higher degree of overlap with each other in the first region compared to the second pixel cells in the second region. The geographic map may be distorted to enlarge the first region and the first pixel cells may be arranged in the first region in a manner that prevents the first pixel cells from overlapping each other. A color value for each of the pixel cells may be determined from a multi-paired color map that represents two variables corresponding to the entities by color and the pixel cells may be caused to be displayed on the distorted geographic map according to the determined respective color values.