Article

A gaussian fields based mining method for semi-automating staff assignment in workflow application

Authors:

Yun YangAuthors Info & Claims

ICSSP '14: Proceedings of the 2014 International Conference on Software and System Process

Pages 178 - 182

https://doi.org/10.1145/2600821.2600843

Published: 26 May 2014 Publication History

Abstract

Staff assignment is a very important task in the research of workflow resource management. Currently, many well-known workflow applications still rely on human resource assigners such as process initiator or process monitor to perform staff assignment task. In this paper, we propose a semi-automatic workflow staff assignment method which can decrease the workload of staff assigner based on a novel semi-supervised machine learning framework. Our method can be applied to learn all kinds of activities that each actor is capable of based on the workflow event log. After we have learned all labeled data, we can suggest a suitable actor to undertake the specified activities when a new process is assigned. With the proposed method, we can get an average prediction accuracy of 97% and 91% on the data sets of two manufacturing enterprise applications respectively.

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Cited By

Wang JYu DLiu C(2024)Task allocation for maximum cooperation in complex structured business processesKnowledge-Based Systems10.1016/j.knosys.2024.111989299:COnline publication date: 5-Sep-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111989
Goel KFehrer TRöglinger MWynn M(2023)Not Here, But There: Human Resource Allocation PatternsBusiness Process Management10.1007/978-3-031-41620-0_22(377-394)Online publication date: 11-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-41620-0_22
Fatima TAzam FAnwar MRasheed Y(2020)A Systematic Review on Software Project Scheduling and Task Assignment ApproachesProceedings of the 2020 6th International Conference on Computing and Artificial Intelligence10.1145/3404555.3404588(369-373)Online publication date: 23-Apr-2020
https://dl.acm.org/doi/10.1145/3404555.3404588

Index Terms

A gaussian fields based mining method for semi-automating staff assignment in workflow application
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications

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Published In

cover image ACM Other conferences

ICSSP '14: Proceedings of the 2014 International Conference on Software and System Process

May 2014

199 pages

ISBN:9781450327541

DOI:10.1145/2600821

General Chair:
He Zhang,
Program Chairs:
LiGuo Huang,
Ita Richardson

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 26 May 2014

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ICSSP '14

ICSSP '14: International Conference on Software and Systems Process 2014

May 26 - 28, 2014

Nanjing, China

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Cited By

Wang JYu DLiu C(2024)Task allocation for maximum cooperation in complex structured business processesKnowledge-Based Systems10.1016/j.knosys.2024.111989299:COnline publication date: 5-Sep-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111989
Goel KFehrer TRöglinger MWynn M(2023)Not Here, But There: Human Resource Allocation PatternsBusiness Process Management10.1007/978-3-031-41620-0_22(377-394)Online publication date: 11-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-41620-0_22
Fatima TAzam FAnwar MRasheed Y(2020)A Systematic Review on Software Project Scheduling and Task Assignment ApproachesProceedings of the 2020 6th International Conference on Computing and Artificial Intelligence10.1145/3404555.3404588(369-373)Online publication date: 23-Apr-2020
https://dl.acm.org/doi/10.1145/3404555.3404588

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