Emc IP Holding Company LLC

David M Dionisio

Raphael Cohen

Anand Singh

Barbara Latulippe

Jenny L Beazley

Perry Davis

Stephen Gatchell

Phu Son Le

Hengli Liang

An apparatus comprises a processing platform configured to implement a machine learning system for automated generation of predicted reliability measures and associated early warning indicators for product and part combinations. The machine learning system comprises a data aggregation module configured to extract product and part data from a big data repository, and a reliability predictor configured to generate predicted reliability measures for respective ones of the product and part combinations utilizing a shared model that is determined based at least in part on the extracted product and part data. The machine learning system processes the predicted reliability measures to generate early warning indicators relating to particular ones of the product and part combinations having predicted reliability measures that fail to meet one or more specified criteria. The machine learning system illustratively provides the early warning indicators to a visualization interface so as to facilitate user adjustment of a product line.

Predicting reliability of product and part combinations using machine learning based on shared model

An apparatus in one embodiment comprises at least one processing platform accessible to a plurality of user devices over at least one network. The processing platform implements a hierarchical value-based governance system for data assets of an enterprise. The hierarchical value-based governance system comprises a data asset governance controller, a technical metadata hub, a business metadata hub and a data asset catalog. The data asset catalog is coupled to the technical metadata hub and the business metadata hub and provides integrated technical metadata and business metadata for each of a plurality of the data assets of the enterprise. The data asset governance controller is configured to generate analytics actions for one or more analytics workspaces based at least in part on the integrated technical metadata and business metadata of the data asset catalog. The analytics actions may comprise visualizations providing actionable insights into the corresponding data assets.

Hierarchical value-based governance architecture for enterprise data assets

An apparatus comprises a processing platform configured to implement an analysis and visualization tool utilizing a mixture of multiple reliability measures to characterize each of a plurality of product and part combinations. The analysis and visualization tool comprises a data aggregation module configured to extract product and part data from a big data repository, a reliability measure generator configured to process the extracted product and part data to generate a plurality of reliability measures for each of a plurality of different product and part combinations, a mixture model module configured to compute a score from the plurality of reliability measures for each of the different product and part combinations, and a visualization module configured to generate at least one visualization as a function of the scores computed for the respective different product and part combinations. The generated visualization may comprise a quadrant plot view visualization displaying the computed scores.

Analysis and visualization tool utilizing mixture of multiple reliability measures for product and part combinations

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Framingham, MA, United States of America

David M Dionisio