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Predictions of Process-Execution Time and Process-Execution Status

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Data-Driven Optimization and Knowledge Discovery for an Enterprise Information System

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Abstract

As shown in Chap. 3, process-execution time is a fundamental measure in an EIS. Our risk-aware execution-time estimation method (Sect. 3.2.2) has demonstrated improved performance over static rule-based methods. However, in addition to performing real-time production scheduling, an EIS should also be able to carry out planning for the future. Therefore, accurate predictions of both process-execution time and process status are crucial for the development of an intelligent EIS. We propose new process-execution time-prediction and process status-prediction methods for an EIS.

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Authors and Affiliations

  1. PayPal, Inc., San Jose, CA, USA

    Qing Duan

  2. ECE, Duke University, Durham, NC, USA

    Krishnendu Chakrabarty

  3. Hewlett-Packard Labs, Palo Alto, CA, USA

    Jun Zeng

Authors
  1. Qing Duan
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  2. Krishnendu Chakrabarty
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  3. Jun Zeng
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Duan, Q., Chakrabarty, K., Zeng, J. (2015). Predictions of Process-Execution Time and Process-Execution Status. In: Data-Driven Optimization and Knowledge Discovery for an Enterprise Information System. Springer, Cham. https://doi.org/10.1007/978-3-319-18738-9_4

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  • DOI: https://doi.org/10.1007/978-3-319-18738-9_4

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18737-2

  • Online ISBN: 978-3-319-18738-9

  • eBook Packages: EngineeringEngineering (R0)

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