지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2012.11
- 수록면
- 916 - 928 (13page)
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초록· 키워드
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This paper demonstrates that the process model discovered from historical event log can be extended to predict business performance and recommend performers of running instances. For the performance prediction and the performer recommendation, we adopt decision tree, which is a decision support tool in management science. Decision trees are commonly used to help identify alternative most likely to reach a goal. The proposed approach is aimed at supporting managers in performer decision-making processes, based on historical data such as completion time and cost according to the performers. To provide effective performer recommendation, we use several filtering
with performers to the decision tree, which allows for a suitable recommendation according to characteristics processes. The proposed technique is evaluated through an experiment using real-life event log in telecom service industry. The main contribution of this paper is to provide a real-time decision support tool by recommending the best performer for a target performance indicator during process execution based on historical data.
with performers to the decision tree, which allows for a suitable recommendation according to characteristics processes. The proposed technique is evaluated through an experiment using real-life event log in telecom service industry. The main contribution of this paper is to provide a real-time decision support tool by recommending the best performer for a target performance indicator during process execution based on historical data.
목차
1. Introduction
2. Related Work
3. Performer Recommendation Using Process Mining
4. Decision Tree Construction for Performance Prediction
5. Decision Tree Matching with Running Case
6. Example Scenario
7. Conclusion and Future Work
References
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UCI(KEPA) : I410-ECN-0101-2014-530-000999624