Hewlett-packard Development Company, L.p.

Hewlett Packard Enterprise Development LP

Hewlett-packard Company

International Business Machines Corporation

Entit Software LLC

Micro Focus Lcc

Other

Ming C Hao

Umeshwar Dayal

Daniel Keim

Meichun Hsu

Nelson L Chang

Halldor Janetzko

Michael Hund

Christian Rohrdantz

Wei-Nchih Lee

Sebastian Mittelstaedt

Alan Hersh Karp

Manish Marwah

Halldór Janetzko

Ratnesh Kumar Sharma

Riddhiman Ghosh

Martha Lurie Lyons

Joseph S Sventek

Daniel A Keim

Markus H Gross

Alex Xin Zhang

Adrian Krug

Peter J Wright

Lars-Erik Haug

Thomas Holenstein

Cullen Edwin Bash

Gregory William Cook

Fabio Casati

Kevin Smathers

Mohamed E Dekhil

Vijay Machiraju

Pankaj K Garg

Ronald L Obermarck

Soma Sundaram Santhiveeran

Klaus Wurster

Daniel Cotting

Gene E Trivett

Walter Hill

Nimesh Patel

Lynn Trivett

Tobias Schreck

Sabastian Mittelstadt

Dominik Morent

Julian Ladisch

Chandrakant D Patel

Keith Farkas

Rajiv Gupta

Chetan Kumar Gupta

Ming-Chien Shan

Renato Keshet

Robert J Dugan

Jerry Zhaolin Shan

Abhay Mehta

Song Wang

Steven Victor Kauffman

Cheng Chang

Bradley J Beitel

Michael E Goss

Robert D Gordon

Robert Lovejoy Raymond

Carl Zeitler, Jr

Paul J Wanish

Ismail Ari

Todd M Spencer

Roger L Cormier

Jishang Wei

Richard R Guyette

Mustazirul Islam

Daniel Garfinkel

Thomas G Fincher

Arun Kumar Paruchuri

Arthur M Sherman

Marie-Jo Fremont

Arthur L Levin

Mark S Anspach

Charles Young

Bao-Yao Zhou

George A McClain

Dominik Jackle

Joem Schneidewind

Toblas Schreck

Stephany Kalil

Betty Cant

Eric Stammers

Brian Ono

Sabastian Mittelstädt

David V Dodd

Sebastian Mittelstädt

Dongman Lee

Mark B Misroch

Lynne C Thieme

Franklin D Snow

Hung C Lieu

Alexander Jaeger

Rodney L Watson

David B Berkowitz

Vineet Singh

Ronald L Hankison

Chandrakat Patel

Justin Aaron Scaggs

Chantal Tremblay

A unified visualization interface is disclosed. One example is a system including an association module, a multicasting module, a data sharing module, and a unified visualization interface. The association module associates an identified event in a first visualization system with a visualization function. The multicasting module stores event data related to the identified event and the associated visualization function in a shared data source, and multicasts the identified event to a second visualization system. The data sharing module associates the event data with characteristics of the first visualization system, and shares, in response to the multicast of the identified event, the shared data source with the second visualization system. The unified visualization interface automatically invokes, without software changes, the second visualization system in response to the multicast of the identified event, the invoking based on the shared data source including the characteristics of the first visualization system.

Automatically invoked unified visualization interface

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

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.

Map with different degrees of overlapping geocoded pixels

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

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.

Non-overlapping visualization of data records of a scatter plot

An event occurring in a particular geographic region is identified based on disseminated information containing public commentary in the particular geographic region. Attributes that are related to the event are identified, and sentiment words relating to the identified event are extracted from the disseminated information, where the extracted sentiment words are in a local language of the particular geographic region. A sentiment trend visualization is generated that depicts a trend of sentiments of at least a particular one of the identified attributes, wherein the sentiments are based on the sentiment words for at least the particular attribute.

Sentiment trent visualization relating to an event occuring in a particular geographic region

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

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.

Data stream processing

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

Growing community of inventors

Palo Alto, CA, United States of America

Ming C Hao