Addressing Process Control Challenges in Big and Wide Data Environments

A number of unique issues must be solved when analyzing big and very high-dimensional data and/or big data with discrete variables of very high cardinality:

  • You must satisfy performance requirements for effective process monitoring and anomaly detection, predictive analytics and fault classification, and root-cause analysis.
  • Discrete predictor variables of high cardinality (for example, codes identifying thousands of tools) must be pre-processed and converted to fewer or to single-column continuous derived variables.
  • Initial feature selection methods must then be applied to derive from the very large numbers of predictor variables a smaller subset of “important” predictors. These are then related to important process outcomes using machine learning algorithms.
  • Results must be delivered to an interactive visualization platform that enables actionable insights for engineers and process stakeholders.

Download the whitepaper to learn about an architecture developed by TIBCO for a large semiconductor manufacturer for efficiently implementing these steps, in addition to real-world analytics use cases typically encountered in this industry.

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