Mining Frequent Instances on Workflows (original) (raw)

Abstract

A workflow is a partial or total automation of a business process, in which a collection of activities must be executed by humans or machines, according to certain procedural rules. This paper deals with an aspect of workflows which has not so far received much attention: providing facilities for the human system administrator to monitor the actual behavior of the workflow system in order to predict the “most probable” workflow executions. In this context, we develop a data mining algorithm for identifying frequent patterns, i.e., the workflow substructures that have been scheduled more frequently by the system. Several experiments show that our algorithm outperforms the standard approaches adapted to mining frequent instances.

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Authors and Affiliations

  1. DEIS, University of Calabria, Via Pietro Bucci, 87036, Rende, Italy
    Gianluigi Greco, Antonella Guzzo & Domenico Saccà
  2. ICAR-CNR, National Research Council, Via Pietro Bucci, 87036, Rende, Italy
    Giuseppe Manco & Domenico Saccà

Authors

  1. Gianluigi Greco
  2. Antonella Guzzo
  3. Giuseppe Manco
  4. Domenico Saccà

Editor information

Editors and Affiliations

  1. Computer Science Department, Korea Advanced Institute of Science and Technology, 373-1 Koo-Sung Dong, Yoo-Sung Ku, Daejeon, 305-701, Korea
    Kyu-Young Whang
  2. Department of Statistics, Seoul National University, Sillimdong Kwanakgu, Seoul, 151-742, Korea
    Jongwoo Jeon
  3. School of Electrical Engineering and Computer Science, Seoul National University, Kwanak P.O. Box 34, Seoul, 151-742, Korea
    Kyuseok Shim
  4. Department of Computer Science and Engineering, University of Minnesota, 200 Union St SE, Minneapolis, MN, 55455, USA
    Jaideep Srivastava

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Greco, G., Guzzo, A., Manco, G., Saccà, D. (2003). Mining Frequent Instances on Workflows. In: Whang, KY., Jeon, J., Shim, K., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2003. Lecture Notes in Computer Science(), vol 2637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36175-8\_21

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