IEICE TRANSACTIONS on Information
A Hybrid Genetic Service Mining Method Based on Trace Clustering Population
Summary :
Service mining aims to use process mining for the analysis of services, making it possible to discover, analyze, and improve service processes. In the context of Web services, the recording of all kinds of events related to activities is possible, which can be used to extract new information of service processes. However, the distributed nature of the services tends to generate large-scale service event logs, which complicates the discovery and analysis of service processes. To solve this problem, this research focus on the existing large-scale service event logs, a hybrid genetic service mining based on a trace clustering population method (HGSM) is proposed. By using trace clustering, the complex service system is divided into multiple functionally independent components, thereby simplifying the mining environment; And HGSM improves the mining efficiency of the genetic mining algorithm from the aspects of initial population quality improvement and genetic operation improvement, makes it better handle large service event logs. Experimental results demonstrate that compare with existing state-of-the-art mining methods, HGSM has better characteristics to handle large service event logs, in terms of both the mining efficiency and model quality.
- Publication
- IEICE TRANSACTIONS on Information Vol.E105-D No.8 pp.1443-1455
- Publication Date
- 2022/08/01
- Publicized
- 2022/04/28
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2021EDP7190
- Type of Manuscript
- PAPER
- Category
- Office Information Systems, e-Business Modeling
Authors
Yahui TANG
Yunnan University
Tong LI
Yunnan Agricultural University
Rui ZHU
Yunnan University
Cong LIU
Shandong University of Technology
Shuaipeng ZHANG
Shandong University of Technology
Keyword
Latest Issue
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Yahui TANG, Tong LI, Rui ZHU, Cong LIU, Shuaipeng ZHANG, "A Hybrid Genetic Service Mining Method Based on Trace Clustering Population" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 8, pp. 1443-1455, August 2022, doi: 10.1587/transinf.2021EDP7190.
Abstract: Service mining aims to use process mining for the analysis of services, making it possible to discover, analyze, and improve service processes. In the context of Web services, the recording of all kinds of events related to activities is possible, which can be used to extract new information of service processes. However, the distributed nature of the services tends to generate large-scale service event logs, which complicates the discovery and analysis of service processes. To solve this problem, this research focus on the existing large-scale service event logs, a hybrid genetic service mining based on a trace clustering population method (HGSM) is proposed. By using trace clustering, the complex service system is divided into multiple functionally independent components, thereby simplifying the mining environment; And HGSM improves the mining efficiency of the genetic mining algorithm from the aspects of initial population quality improvement and genetic operation improvement, makes it better handle large service event logs. Experimental results demonstrate that compare with existing state-of-the-art mining methods, HGSM has better characteristics to handle large service event logs, in terms of both the mining efficiency and model quality.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDP7190/_p
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@ARTICLE{e105-d_8_1443,
author={Yahui TANG, Tong LI, Rui ZHU, Cong LIU, Shuaipeng ZHANG, },
journal={IEICE TRANSACTIONS on Information},
title={A Hybrid Genetic Service Mining Method Based on Trace Clustering Population},
year={2022},
volume={E105-D},
number={8},
pages={1443-1455},
abstract={Service mining aims to use process mining for the analysis of services, making it possible to discover, analyze, and improve service processes. In the context of Web services, the recording of all kinds of events related to activities is possible, which can be used to extract new information of service processes. However, the distributed nature of the services tends to generate large-scale service event logs, which complicates the discovery and analysis of service processes. To solve this problem, this research focus on the existing large-scale service event logs, a hybrid genetic service mining based on a trace clustering population method (HGSM) is proposed. By using trace clustering, the complex service system is divided into multiple functionally independent components, thereby simplifying the mining environment; And HGSM improves the mining efficiency of the genetic mining algorithm from the aspects of initial population quality improvement and genetic operation improvement, makes it better handle large service event logs. Experimental results demonstrate that compare with existing state-of-the-art mining methods, HGSM has better characteristics to handle large service event logs, in terms of both the mining efficiency and model quality.},
keywords={},
doi={10.1587/transinf.2021EDP7190},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - A Hybrid Genetic Service Mining Method Based on Trace Clustering Population
T2 - IEICE TRANSACTIONS on Information
SP - 1443
EP - 1455
AU - Yahui TANG
AU - Tong LI
AU - Rui ZHU
AU - Cong LIU
AU - Shuaipeng ZHANG
PY - 2022
DO - 10.1587/transinf.2021EDP7190
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2022
AB - Service mining aims to use process mining for the analysis of services, making it possible to discover, analyze, and improve service processes. In the context of Web services, the recording of all kinds of events related to activities is possible, which can be used to extract new information of service processes. However, the distributed nature of the services tends to generate large-scale service event logs, which complicates the discovery and analysis of service processes. To solve this problem, this research focus on the existing large-scale service event logs, a hybrid genetic service mining based on a trace clustering population method (HGSM) is proposed. By using trace clustering, the complex service system is divided into multiple functionally independent components, thereby simplifying the mining environment; And HGSM improves the mining efficiency of the genetic mining algorithm from the aspects of initial population quality improvement and genetic operation improvement, makes it better handle large service event logs. Experimental results demonstrate that compare with existing state-of-the-art mining methods, HGSM has better characteristics to handle large service event logs, in terms of both the mining efficiency and model quality.
ER -
English
