Abstract
Massive amounts of business process event logs are collected and stored by modern information systems. Numerous process discovery approaches have been proposed to extract descriptive process models from such event logs in the past decades. To improve process discovery efficiency, event log sampling techniques are proposed. A sample log is a delicately selected subset of the original log that requires less computational cost. However, existing sampling techniques have difficulties, e.g., low efficiency, in handling large-scale event logs. To tackle this challenge, we propose a novel ranking-based event log sampling approach, denoted as \( LogRank^+ \), to support efficient sampling. In addition, we introduce a framework to evaluate the effectiveness of different sampling techniques by quantifying the sampling efficiency and the quality of sample logs. The proposed sampling approach has been implemented in the open-source process mining toolkit ProM. Experimental evaluation with both synthetic and real-life event logs demonstrates that the proposed sampling approach provides an effective solution to improve event log sampling efficiency as well as ensuring high quality of the obtained sample logs from a process discovery perspective.
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Acknowledgement
This work was supported in part by National Natural Science Foundation of China under Grant 61902222, Science and Technology Development Fund of Shandong Province of China under Grant ZR2017MF027, the Taishan Scholars Program of Shandong Province under Grants ts20190936 and tsqn201909109, SDUST Research Fund under Grant 2015TDJH102.
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Liu, C., Pei, Y., Zeng, Q., Duan, H., Zhang, F. (2020). \( LogRank^+ \): A Novel Approach to Support Business Process Event Log Sampling. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2020. WISE 2020. Lecture Notes in Computer Science(), vol 12343. Springer, Cham. https://doi.org/10.1007/978-3-030-62008-0_29
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DOI: https://doi.org/10.1007/978-3-030-62008-0_29
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