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Authors: Liu Yingbo 1 ; Wang Jianmin 1 and Sun Jiaguang 2

Affiliations: 1 School of Software, Tsinghua University, China ; 2 School of Information Science and Technology, Tsinghua University, China

ISBN: 978-972-8865-89-4

ISSN: 2184-4992

Keyword(s): Time analysis, Workflow management system, Machine learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence and Decision Support Systems ; Enterprise Information Systems ; Industrial Applications of Artificial Intelligence

Abstract: Activity time consumption knowledge is essential to successful scheduling in workflow applications. However, the uncertainty of activity execution duration in workflow applications makes it a non-trivial task for schedulers to appropriately organize the ongoing processes. In this paper, we present a K-level prediction approach intended to help workflow schedulers to anticipate activities' time consumption. This approach first defines K levels as a global measure of time. Then, it applies a decision tree learning algorithm to the workflow event log to learn various kinds of activities' execution characteristics. When a new process is initiated, the classifier produced by the decision tree learning technique takes prior activities' execution information as input and suggests a level as the prediction of posterior activity's time consumption. In the experiment on three vehicle manufacturing enterprises, 896 activities were investigated, and we separately achieved and average prediction a ccuracy of 80.27%, 70.93% and 61.14% with K = 10. We also applied our approach on greater values of K, however the result is less positive. We describe our approach and report on the result of our experiment. (More)

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Paper citation in several formats:
Yingbo, L.; Jianmin, W. and Jiaguang, S. (2007). USING DECISION TREE LEARNING TO PREDICT WORKFLOW ACTIVITY TIME CONSUMPTION.In Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-972-8865-89-4, pages 69-75. DOI: 10.5220/0002404900690075

@conference{iceis07,
author={Liu Yingbo and Wang Jianmin and Sun Jiaguang},
title={USING DECISION TREE LEARNING TO PREDICT WORKFLOW ACTIVITY TIME CONSUMPTION},
booktitle={Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2007},
pages={69-75},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002404900690075},
isbn={978-972-8865-89-4},
}

TY - CONF

JO - Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - USING DECISION TREE LEARNING TO PREDICT WORKFLOW ACTIVITY TIME CONSUMPTION
SN - 978-972-8865-89-4
AU - Yingbo, L.
AU - Jianmin, W.
AU - Jiaguang, S.
PY - 2007
SP - 69
EP - 75
DO - 10.5220/0002404900690075

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