Hewlett-Packard Development Company, L.p.

Entit Software LLC

Other

Hewlett Packard Enterprise Development LP

Wei-Nchih Lee

Ming C Hao

Daniel Keim

Nelson L Chang

Michael Hund

Halldor Janetzko

Sebastian Mittelstaedt

Martin Arlitt

Jerome Alexander Rolia

Kevin Smathers

Gowtham Bellala

Jishang Wei

Dominik Jackle

Jose Alberto Cueto Barcenas

Sherif Abdelwahab

Alexander Jaeger

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

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

Overlay networks of application components are managed. Applicant components may create overlay networks based on policies of the application components and an environment of the overlay network. The overlay network may be adjusted based on changes to the policies or the environment.

Overlay network management

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

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Palo Alto, CA, United States of America

Wei-Nchih Lee