loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

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

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 accuracy 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)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 44.200.237.112

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

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; ISSN 2184-4992, SciTePress, 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},
issn={2184-4992},
}

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
IS - 2184-4992
AU - Yingbo, L.
AU - Jianmin, W.
AU - Jiaguang, S.
PY - 2007
SP - 69
EP - 75
DO - 10.5220/0002404900690075
PB - SciTePress