$$ LogRank^+ $$: A Novel Approach to Support Business Process Event Log Sampling

Liu, Cong; Pei, Yulong; Zeng, Qingtian; Duan, Hua; Zhang, Feng

doi:10.1007/978-3-030-62008-0_29

Cited by 1 publication

(3 citation statements)

References 18 publications

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“…In our experiment, seven sampling strategies, including BF-Sampling − (Brute Force Sampling − ), BF-Sampling + (Brute Force Sampling + ), in the paper is compared to seven state-of-the-art sampling techniques including LogRank-based sampling, 22 LogRank + -based sampling, 24 LogRank ++ -based sampling, 25 Similarity-based sampling, 26 Frequency-based sampling, 26 Longer-based sampling, 26 and Shorter-based were compared.…”

Section: Experimental Settingmentioning

confidence: 99%

“…However, the performance of the LogRank‐based sampling technique is poor even for medium‐sized event logs. To improve sampling efficiency, LogRank

{}^{+}

‐based sampling technique is proposed by calculating the similarity between a trace and the rest of the whole log to be used as the importance value in Reference 24. However, this technique requires a large search space, and the sampling rate needs to be predefined by users.…”

Section: Related Workmentioning

confidence: 99%

“…However, this technique requires a large search space, and the sampling rate needs to be predefined by users. More recently, to further improve sampling efficiency, LogRank

{}^{++}

‐based sampling technique is proposed in Reference 25, which quantifies the importance of traces based on activity and DFR. Although the sampling efficiency is greatly accelerated, the completeness of the sample log against the original one cannot be guaranteed, that is, important behavior is missing during the sampling phase.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Sampling business process event logs with guarantees

Su,

Liu,

Zhang

et al. 2024

Concurrency and Computation

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SummaryEvent log sampling has emerged as a key research focus in the field of process mining, aiming to enhance the efficiency of various process mining tasks, including model discovery, conformance checking, and process prediction. However, current log sampling techniques often fail to ensure high‐quality sample logs. This paper introduces a novel framework to support efficient event log sampling without compromising the quality of the sample log compared to the original one. The approach revolves around the consideration of directly‐follows relation (DFR) among business tasks as the fundamental behavior unit of an event log. By ensuring the DFR equivalence between the original and sample logs, the proposed technique addresses the challenge of sample log quality from the model discovery point of view. The framework is instantiated by seven distinct sampling strategies each has its own specialty and is fully implemented in the open‐source process mining tool platform ProM. To validate its effectiveness, we conducted a comprehensive experimental evaluation using 12 publicly available real‐life event logs against state‐of‐the‐art sampling techniques. The results clearly demonstrate that our technique significantly improves model discovery efficiency while upholding high quality of the discovered models.

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Section: Experimental Settingmentioning

confidence: 99%

“…However, the performance of the LogRank‐based sampling technique is poor even for medium‐sized event logs. To improve sampling efficiency, LogRank

{}^{+}

Section: Related Workmentioning

confidence: 99%

“…However, this technique requires a large search space, and the sampling rate needs to be predefined by users. More recently, to further improve sampling efficiency, LogRank