Ca, Inc.

Ye Chen

Jin Zhang

Chi Zhang

Steven Lloyd Greenspan

Maria C Velez-Rojas

Yue Xiao

Serge Mankovskii

Lan Xu

Provided is a process of configuring a dashboard of a graphical user interface, the process including: obtaining identifiers of metrics; obtaining features that are properties of the metrics; accessing in a graph-effectiveness matrix effectiveness scores corresponding to the features; selecting a plurality of graphs to graphically represent the metrics in a dashboard; and instructing a computing device to display the dashboard.

Predictive graph selection

Provided is a process of conducting a collaborative session between two analytics graphical user interfaces (GUI), the process including: instructing a first computing device associated with a first user to display a first GUI having a first graph depicting a first set of values of a first metric; determining that the first graph is to be shared on a second computing device associated with a second user in a second GUI; inferring that the second user prefers to view the first metric in a second graph based on a record of previous interactions in which the second graph was selected to view the first metric; and in response, instructing the second computing device to display in the second GUI the second graph depicting at least some of the first set of values of the first metric.

Collaborative data analytics application

A data-driven big data mining and reporting system automatically identifies which data attributes to report from a first data set using natural language processing. The identified data attributes to report from the first data set is used to automatically extract additional data attributes to report from additional data sets so that the identified data attributes to report from the first data set and the extracted data attributes to report from the additional data sets can be reported without input from the end user.

Automatic natural language processing based data extraction

A big data processing system includes a features permutations testing function that separates out from among a set of identified compound features, those compound feature permutations that have better capabilities for distinguishing between anomalies observed in respective multi-dimensional feature spaces having as their axes the features of the identified compound features.

Identification of distinguishing compound features extracted from real time data streams

A big data processing system includes a workload trimming function that separates out from among a set of identified anomalies, those that are clearly outliers, rather than ones residing within clusters of anomalies as mapped within an anomalies distribution space. The outlier anomalies are not subjected to a computationally-intensive anomalies aggregating process and thus, processing resources are conserved.

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Milpitas, CA, United States of America

Ye Chen