Tibco Software Inc.

Other

Steven Hillion

David M Katz

Thomas A Hill

Yi-Ling Chen

Piotr Smolinski

Siva Ramalingam

Lawrence Spracklen

Zhe Dong

Yong-Sheng Yu

Michael Thyen

Yong Zhao

A process control tool for processing wide data from automated manufacturing operations. The tool including a feature selector, an analysis server, and a visualization engine. The feature selector receives process input data from at least one manufacturing process application, wherein the process input data includes a plurality of observations and associated variables, converts the received process input data to a stacked format having one row for each variable in each observation, converts identified categorical variables into numerical variables and identified time-series data into fixed numbers of intervals, computes statistics that measure the strengths of relationships between predictor values and an outcome variable, orders, filters, and pivots the predictor values. The analysis server performs at least one operation to identify interactions between predictor values, e.g. using maximum Likelihood computations or predefined searches, in the filtered predictor values. The visualization engine displays the interactions for use in managing the manufacturing operations.

Process control tool for processing big and wide data

Embodiments are described for a system and method to analyze data at a plurality of data sources. A data analytic workflow may be received. The data analytic workflow may include at least one operation to be performed on a plurality of data sets stored at a plurality of data sources. Instructions may be created based on the operation to be performed and a type of platform that operates the data sources. Furthermore, the instructions may be transmitted to the data sources such that the data sources may execute the operations on the data sets stored at the data sources.

Systems and methods for analysis of data at disparate data sources

Platform-agnostic predictive models based on database management system instructions are described. A system identifies a representation of data transformations associated with a first predictive model that executes on a first computing platform. The system parses the representation of data transformations. The system generates database management system instructions that correspond to the parsed representation of data transformations. The system sends the database management system instructions to a second predictive model that executes on a second computing platform, thereby enabling the second predictive model to execute at least some of the database management system instructions to generate a prediction. The first computing platform and the second computing platform are different types of computing platforms.

Platform-agnostic predictive models based on database management system instructions

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San Francisco, CA, United States of America

Steven Hillion