Hewlett-packard Development Company, L.p.

Micro Focus Lcc

Hewlett Packard Enterprise Development LP

Entit Software LLC

Nelson L Chang

Ming C Hao

Daniel Keim

Michael Hund

Wei-Nchih Lee

Jishang Wei

Henry W Sang, Jr

Mithra Vankipuram

Kevin Smathers

Arun Kumar Paruchuri

Ian N Robinson

Halldor Janetzko

Sebastian Mittelstaedt

Bradley Wittke

Warren Bruce Jackson

Meichun Hsu

Gregory William Cook

Riddhiman Ghosh

Chih Pin Hsiao

Renato Keshet

Hiroshi Horii

Irwin E Sobel

Joshua Hailpern

Will Allen

Alexander Thayer

Christian Rohrdantz

Ji Won Jun

Irwin Sobel

Alexander Jaeger

Jinrong Xie

Curtis N Thetford

Enrique Alejandro Pena Arenzana

Juan Pablo Sepulveda Lopez

Jason Jewell

Dominik Jackle

Felix Hamborg

Justin Aaron Scaggs

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

Examples disclosed herein relate to viewing device adjustment based on eye accommodation in relation to a display. In one implementation, a computing device determines accommodation information related to a viewer's focus on an image rendered on a display. The computing device may cause an optical component associated with a viewing device to adjust based on the determined accommodation.

Viewing device adjustment based on eye accommodation in relation to a display

An example method is provided in according with one implementation of the present disclosure. The method includes identifying an intention of a user of a system in relation to a three-dimensional (3D) virtual object and selecting a 3D navigation mode from a plurality of 3D navigation modes based on the identified user intention. The plurality of 3D navigation modes includes at least a model navigation mode, a simple navigation mode, a driving navigation mode, a reaching navigation mode, and a multi-touch navigation mode. The method further includes transitioning the system to the selected 3D navigation mode.

3D navigation mode

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

In one example of the disclosure, presence data indicative of a presence factor for a remote subject is received from a server. The presence data is analyzed and a display representative of the presence factor is caused at a 3D object. The 3D object includes a 3D element representative of the remote subject.

Displays representative of remote subjects

Examples associated with display mapping are described. One example system includes a display mapping module. The display mapping module maps display components connected to the system to virtual channels to which the system is subscribed. A communication module transmits a content instruction to subscribers of a virtual channel. The instruction controls the subscribers of the virtual channel to display content associated with the content instruction on display components the respective subscribers have mapped to the virtual channel. A display module causes a display component mapped to the virtual channel to display content associated with the content instruction.

Display mapping

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

A technique for force feedback can include displaying a data set in a visual model. A technique for force feedback can include selecting a portion of the data instances in the data set. A technique for force feedback can also include providing a force feedback via a force feedback device as an interaction point interacts with the portion of the data instances.

Force feedback

A visualization includes a multidimensional array of multidimensional graphlets, the multidimensional array arranging the multidimensional graphlets in a plurality of dimensions, where each of the multidimensional graphlets includes at least three dimensions. Respective attributes of data are represented with the plurality of dimensions of the multidimensional array. Respective additional attributes of the data are represented with the dimensions of the multidimensional graphlets.

Visualization including multidimensional graphlets

Interactive analysis of data based on progressive visualizations is disclosed. One example is a system including a data module, a visualization module, an interaction module, and an analytics module. The data module processes a plurality of data elements. The visualization module progressively displays, via an interactive graphical user interface, an overview visualization of a sub-plurality of the plurality of data elements, the overview visualization based on at least one of data resolution and display resolution, and a detailed visualization based on an interaction with the overview visualization. The interaction module processes the interaction with the overview visualization. The analytics module analyzes the interaction to generate a deployment rule.

Interactive analysis of data based on progressive visualizations

An example system is provided in according with one implementation of the present disclosure. The system includes a 3D display displaying at least one three-dimensional (3D) visualization, an auxiliary computing device including a multi-touch display and a plurality of sensors, and a behavior analysis engine to perform a behavior analysis of a user by using data from the plurality of sensors. The behavior analysis engine is to: determine an attention engagement level of the user, and determine a pose of the user in relation to the auxiliary computing device. The system further includes an intention analysis engine to determine an intention of the user in relation to the at least one 3D visualization based on the user's attention engagement level and the user's pose, and an interaction mode engine to automatically adjust the system to an interaction mode based on the identified user intention.

3D visualization

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.

Growing community of inventors

San Jose, CA, United States of America

Nelson L Chang