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

Xu, Rongbin, Liu, Xiao, Xie, Ying, Yuan, Dong and Yang, Yun 2014, A Gaussian fields based mining method for semi-automating staff assignment in workflow application, in ICSSP 2014 : Proceedings of the Software and Systems Process 2014 Conference, ACM,, pp. 178-182, doi: 10.1145/2600821.2600843.

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Title A Gaussian fields based mining method for semi-automating staff assignment in workflow application
Author(s) Xu, Rongbin
Liu, XiaoORCID iD for Liu, Xiao orcid.org/0000-0001-8400-5754
Xie, Ying
Yuan, Dong
Yang, Yun
Conference name Software and Systems Process. Conference (2014 : Nanjing, China)
Conference location Nanjing, China
Conference dates 2014/05/26 - 2014/05/28
Title of proceedings ICSSP 2014 : Proceedings of the Software and Systems Process 2014 Conference
Editor(s) [Unknown]
Publication date 2014
Start page 178
End page 182
Total pages 5
Publisher ACM
Keyword(s) staff assignment
resource management
Gaussian fields
Summary 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.
ISBN 9781450327541
Language eng
DOI 10.1145/2600821.2600843
Field of Research 080608 Information Systems Development Methodologies
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, ACM
Persistent URL http://hdl.handle.net/10536/DRO/DU:30087730

Document type: Conference Paper
Collection: School of Information Technology
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