The Impact of Event Log Subset Selection on the Performance of Process Discovery Algorithms

Sani, Mohammadreza Fani; Zelst, Sebastiaan J. van; Aalst, Wil M. P. van der

doi:10.1007/978-3-030-30278-8_39

Cited by 14 publications

(10 citation statements)

References 15 publications

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“…Finally, all these sampling methods are unbiased and consequently they leads to non-deterministic results. In [19], we analyze random and biased sampling methods with which we are able to adjust the size of the sampled data for process discovery. Moreover, [36] shows that using a clustering-based instance selection method will provide more precise and simpler process models.…”

Section: Related Workmentioning

confidence: 99%

Conformance Checking Approximation Using Subset Selection and Edit Distance

Sani

Zelst

Aalst

2020

Lecture Notes in Computer Science

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Conformance checking techniques let us find out to what degree a process model and real execution data correspond to each other. In recent years, alignments have proven extremely useful in calculating conformance statistics. Most techniques to compute alignments provide an exact solution. However, in many applications, it is enough to have an approximation of the conformance value. Specifically, for large event data, the computation time for alignments is considerably long using current techniques which makes them inapplicable in reality. Also, it is no longer feasible to use standard hardware for complex process models. This paper, proposes new approximation techniques to compute approximated conformance checking values close to exact solution values in less time. These methods also provide upper and lower bounds for the approximated alignment value. Our experiments on real event data show that it is possible to improve the performance of conformance checking by using the proposed methods compared to using the state-of-the-art alignment approximation technique. Results show that in most of the cases, we provide tight bounds, accurate approximated alignment values, and similar deviation statistics.

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Section: Related Workmentioning

confidence: 99%

Conformance Checking Approximation Using Subset Selection and Edit Distance

Sani

Zelst

Aalst

2020

Lecture Notes in Computer Science

Self Cite

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“…Research like [7] has shown that by using only a small subset of traces for process discovery we sometimes can improve the quality of process models. The main challenge faced this research is which traces should be selected as input for process discovery algorithms.…”

Section: Motivating Examplementioning

confidence: 99%

“…The main challenge faced this research is which traces should be selected as input for process discovery algorithms. Some methods, e.g., [8,7], propose to use sampling methods for this purpose without considering the quality of discovered model during the selection phase. We aim to find the most representative process instances of a log, i.e., referred to prototypes, using a clustering method.…”

Section: Motivating Examplementioning

confidence: 99%

“…Note that the statistical sampling method [8] returns all the traces to have a high confidence of not loosing information (and because of the small size of the log). The biased sampling method [7] takes as input the percentage of desired traces. In this example, we used 30% of the entire log to get the same number of traces as our method.…”

Section: Motivating Examplementioning

confidence: 99%

“…Another way to reduce log variability that causes simpler models is to extract only a small set of traces. Authors in [7] and [8] present different trace selection methods that improve the performance of process discovery algorithms using random and biased sampling. These works are close to the present paper's method as the size of the reduced log is considerably smaller than the original one.…”

Section: Related Workmentioning

confidence: 99%

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Prototype Selection Using Clustering and Conformance Metrics for Process Discovery

Sani

Boltenhagen

Aalst

2020

Business Process Management Workshops

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Automated process discovery algorithms aim to automatically create process models based on event data that is captured during the execution of business processes. These algorithms usually tend to use all of the event data to discover a process model. Using all (i.e., less common) behavior may lead to discover imprecise and/or complex process models that may conceal important information of processes. In this paper, we introduce a new incremental prototype selection algorithm based on the clustering of process instances to address this problem. The method iteratively computes a unique process model from a different set of selected prototypes that are representative of whole event data and stops when conformance metrics decrease. This method has been implemented using both ProM and RapidProM. We applied the proposed method on several real event datasets with state-of-the-art process discovery algorithms. Results show that using the proposed method leads to improve the general quality of discovered process models.

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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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The Impact of Event Log Subset Selection on the Performance of Process Discovery Algorithms

Cited by 14 publications

References 15 publications

Conformance Checking Approximation Using Subset Selection and Edit Distance

Conformance Checking Approximation Using Subset Selection and Edit Distance

Prototype Selection Using Clustering and Conformance Metrics for Process Discovery

Sampling business process event logs with guarantees

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