An Efficient Heuristic Method for Repairing Event Logs Independent of Process Models

Li Kong, Chuanyi Li, Jidong Ge, Zhongjin Li, Feifei Zhang, Bin Luo

Abstract

Due to the big volume of data and complex execution, event logs of business processes inevitably contain various errors. In the field of process mining, if we derive process models from the event data without repairing, it is very likely that the resulting process is extremely different from what we expect. Current methods of repairing logs generally compare the log with an existing reference model to seek an optimal alignment, which requires that there should be a reliable reference model. Therefore, this paper presents an approach which only refers to the log itself to repair mistaken traces. We identify loop structures and frequent event sequences (sound conditions) between certain events. For each trace, basic trace and loop events are separated in advance. The basic trace is split into several parts to get repaired one by one according to sound conditions. Then loop events are added back and checked according to corresponding loop structure we discover. The repaired log should be as clean as possible and as similar to the original log as possible so that correctness and integrity of the original log are guaranteed. Experimental results based on different logs prove that our approach is effective and efficient.

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Paper Citation


in Harvard Style

Kong L., Li C., Ge J., Li Z., Zhang F. and Luo B. (2019). An Efficient Heuristic Method for Repairing Event Logs Independent of Process Models.In Proceedings of the 4th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-369-8, pages 83-93. DOI: 10.5220/0007676400830093


in Bibtex Style

@conference{iotbds19,
author={Li Kong and Chuanyi Li and Jidong Ge and Zhongjin Li and Feifei Zhang and Bin Luo},
title={An Efficient Heuristic Method for Repairing Event Logs Independent of Process Models},
booktitle={Proceedings of the 4th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},
year={2019},
pages={83-93},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007676400830093},
isbn={978-989-758-369-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - An Efficient Heuristic Method for Repairing Event Logs Independent of Process Models
SN - 978-989-758-369-8
AU - Kong L.
AU - Li C.
AU - Ge J.
AU - Li Z.
AU - Zhang F.
AU - Luo B.
PY - 2019
SP - 83
EP - 93
DO - 10.5220/0007676400830093