Hewlett-packard Development Company, L.p.

Hewlett Packard Enterprise Development LP

At+t Corp.

Micro Focus Lcc

Entit Software LLC

At&t Intellectual Property I, L.p.

Daniel Keim

Ming C Hao

Umeshwar Dayal

Meichun Hsu

Nelson L Chang

Halldor Janetzko

Michael Hund

Wei-Nchih Lee

Sebastian Mittelstaedt

Manish Marwah

Halldór Janetzko

Stephen Charles North

Christian Panse

Martha Lurie Lyons

Christian Rohrdantz

Soma Sundaram Santhiveeran

Walter Hill

Tobias Schreck

Cullen Edwin Bash

Ratnesh Kumar Sharma

Fabio Casati

Kevin Smathers

Riddhiman Ghosh

Jishang Wei

Lars-Erik Haug

Sabastian Mittelstadt

Dominik Morent

Justin Aaron Scaggs

Eric Stammers

Felix Hamborg

Dominik Jackle

Sabastian Mittelstädt

Sebastian Mittelstädt

Alexander Jaeger

Toblas Schreck

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.

Generating cohorts using automated weighting and multi-level ranking

Similarities between events that include a plurality of dimensions are computed, the similarities computed based on binary comparisons between the events and based on user-specified weights for the dimensions. Multidimensional scaling (MDS) values are calculated based on the computed similarities between the events. A graphical visualization is generated of a temporal plot of the events, the temporal plot comprising a first axis corresponding to time, and a second axis corresponding to the MDS values, and the temporal plot representing overlapping time slices each containing pixels representing a respective subset of the events.

Pixel-based temporal plot of events according to multidimensional scaling values based on event similarities and weighted dimensions

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.

Iterative visualization of a cohort for weighted high-dimensional categorical data

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.

Visualizing topics with bubbles including pixels

Visually interactive identification of a cohort of similar data objects is disclosed. One example is a system including a data processor to access a plurality of data objects, each data object comprising a plurality of numerical components, where each component represents a data feature of a plurality of data features, and to identify, for each data feature, a feature distribution of the numerical components. A selector selects a sub-plurality of the data features of a query object, where a given data feature is selected if the component representing the given data feature is a peak for the feature distribution. An evaluator determines a similarity measure based on the sub-plurality of the data features. An interaction processor iteratively processes selection of a sub-plurality of the data features based on domain knowledge, and identifies, based on the similarity measures, a cohort of data objects similar to the query object.

Visually interactive identification of a cohort of data objects similar to a query based on domain knowledge

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.

Displaying visual analytics of entity data

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.

Facilitating interpretation of high-dimensional data clusters

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.

Visualization that indicates event significance represented by a discriminative metric computed using a contingency calculation

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.

Generalized scatter plots

Example embodiments relate to providing visual analytics of spatial time series data. In example embodiments, sensors may be located at regions within a building for collecting sensor data at regular time intervals. A sensor hierarchy can be generated including sensor nodes that are hierarchically arranged according to a physical infrastructure of the building, where each of the sensor nodes corresponds to a sensor. Sensor data can be obtained from the sensors, and a pixel calendar tree can be generated based on the sensor data and the sensor hierarchy, where the pixel calendar tree is recursively subdivided into tree portions according to a proportion of the sensor data attributable to each of the sensors. The pixel calendar tree can be displayed, where each of the tree portions includes time series sensor data of a corresponding region that is generated based on the sensor data.

Visual analytics of spatial time series data using a pixel calendar tree

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.

Visualizing motifs with visual structures

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.

Computing predicted data according to weighted peak preservation and time distance biasing

For plural categorical values of a first attribute, more relevant categorical values of a second attribute are selected from among candidate categorical values of the second attribute, where the selecting uses a relevance determination process that considers frequencies of occurrence of respective pairs of the categorical values of the first and second attributes. A visualization that includes groups of cells corresponding to the respective pairs of categorical values of the first and second attributes is generated. At least one of the groups includes cells assigned visual indicators based on values of a third attribute.

Visualizing a relationship of attributes using a relevance determination process to select from candidate attribute values

Visual analytics for multivariate session data using concentric rings with overlapping periods includes displaying an interactive graph in a display. The interactive graph includes at least a portion of multiple concentric rings where each one of at least some of the multiple concentric rings represents periodic time units. At least some of the multiple concentric rings are divided into cells where the cells represent smaller time periods than the time units. A color of each of the cells represents a value of a metric. Also, an overlapping period ring displayed with the multiple concentric rings where the overlapping period ring comprises segments that represent overlapping metrics from the cells of the concentric rings that correspond with the segments.

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Steisslingen, Germany

Daniel Keim