Conformance Checking Approximation Using Subset Selection and Edit Distance

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

doi:10.1007/978-3-030-49435-3_15

Cited by 18 publications

(18 citation statements)

References 26 publications

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“…The authors of [18] propose a conformance approximation method that applies relaxation labeling methods on a partial order representation of a process model, which needs to preprocess the process model each time. Unlike these approaches that do not provide bounds for the approximation, some methods have proposed to generate a subset of model behaviors using instance selection [6] and simulation [19]. [6] has proposed to compute alignments of some instances in the event log and use the corresponding model behavior for approximating the alignment of other instances.…”

Section: Related Workmentioning

confidence: 99%

“…Unlike these approaches that do not provide bounds for the approximation, some methods have proposed to generate a subset of model behaviors using instance selection [6] and simulation [19]. [6] has proposed to compute alignments of some instances in the event log and use the corresponding model behavior for approximating the alignment of other instances.…”

Section: Related Workmentioning

confidence: 99%

“…Sampling Proxy-sets can be generated using sampling methods: either sampling members for the input event log, the given process model, or a mixture thereof. In previous work, we investigated sampling of model behavior using uniform distributions [6] and event-log-guided process model simulation [19].…”

Section: Generating Proxy-setsmentioning

confidence: 99%

“…Recently, various approaches for alignment approximation have been proposed [6,7]. In our previous work [6], we exploit subsets of the process model's behavior for approximation, i.e., by using the subset of process behavior as a representative for the complete process model behavior. In this way, we are able to provide bounds for the approximated alignment value.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Model Independent Error Bound Estimation for Conformance Checking Approximation

Sani,

Kabierski,

van Zelst

et al. 2021

Preprint

Self Cite

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Conformance checking techniques allow us to quantify the correspondence of a process's execution, captured in event data, w.r.t., a reference process model. In this context, alignments have proven to be useful for calculating conformance statistics. However, for extensive event data and complex process models, the computation time of alignments is considerably high, hampering their practical use. Simultaneously, it suffices to approximate either alignments or their corresponding conformance value(s) for many applications. Recent work has shown that using subsets of the process model behavior leads to accurate conformance approximations. The accuracy of such an approximation heavily depends on the selected subset of model behavior. Thus, in this paper, we show that we can derive a priori error bounds for conformance checking approximation based on arbitrary activity sequences, independently of the given process model. Such error bounds subsequently let us select the most relevant subset of process model behavior for the alignment approximation. Experiments confirm that conformance approximation accuracy improves when using the proposed error bound approximation to guide the selection of relevant subsets of process model behavior.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Generating Proxy-setsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Model Independent Error Bound Estimation for Conformance Checking Approximation

Sani,

Kabierski,

van Zelst

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…[26] studies some behavior qualities for sampled event logs. In [22], the authors show that by selecting a few unique process instances of event data, it is possible to approximate the conformance value in a shorter time. In [12], the authors proposed a sampling approach based on the Parikh vector of traces to detect the behavior in the event log.…”

Section: Related Workmentioning

confidence: 99%

Improving the performance of process discovery algorithms by instance selection

2020

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Process discovery algorithms automatically discover process models based on event data that is captured during the execution of business processes. These algorithms tend to use all of the event data to discover a process model. When dealing with large event logs, it is no longer feasible using standard hardware in limited time. A straightforward approach to overcome this problem is to down-size the event data by means of sampling. However, little research has been conducted on selecting the right sample, given the available time and characteristics of event data. This paper evaluates various subset selection methods and evaluates their performance on real event data. The proposed methods have been implemented in both the ProM and the RapidProM platforms. Our experiments show that it is possible to considerably speed up discovery using instance selection strategies. Furthermore, results show that applying biased selection of the process instances compared to random sampling will result in simpler process models with higher quality.

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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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Conformance Checking Approximation Using Subset Selection and Edit Distance

Cited by 18 publications

References 26 publications

Model Independent Error Bound Estimation for Conformance Checking Approximation

Model Independent Error Bound Estimation for Conformance Checking Approximation

Improving the performance of process discovery algorithms by instance selection

Sampling business process event logs with guarantees

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