Accurate predictions of process-execution time and process status based on support-vector regression for enterprise information systems

Published

Journal Article

© 1982-2012 IEEE. Accurate predictions of both process-execution time and process status are crucial for the development of an intelligent enterprise information system (EIS). We have developed new automated learning-based process-execution time-prediction and process status-prediction methods that can be embedded into an EIS. Process-execution time prediction is a regression problem and state-of-the-art (baseline) time-prediction methods use a machine-learning regression model. Process status prediction is a binary classification problem in which a class labeled "completed" or "in-progress" is assigned to a process with respect to an arbitrary predictive horizon (i.e., the future time given by the method user). The methods proposed in this paper integrate statistical methods with support-vector regression. Comparison results obtained from the real data of a digital-print enterprise show that the proposed time-prediction method reduces both the relative mean error and the root-mean-squared error of the regression model. Furthermore, the proposed status-prediction method not only achieves higher classification accuracy than state-of-the-art methods, it also estimates the probability of the predicted status. In addition, algorithm development and training phases of the proposed methods do not rely on any arbitrary predictive horizon. Therefore, a single time-prediction model as proposed is sufficient for status prediction as opposed to a baseline status-prediction method that requires classification models for all potential predictive horizons.

Full Text

Duke Authors

Cited Authors

  • Duan, Q; Zeng, J; Chakrabarty, K; Dispoto, G

Published Date

  • March 1, 2015

Published In

Volume / Issue

  • 34 / 3

Start / End Page

  • 354 - 366

International Standard Serial Number (ISSN)

  • 0278-0070

Digital Object Identifier (DOI)

  • 10.1109/TCAD.2014.2387831

Citation Source

  • Scopus