Estimating Process Conformance by Trace Sampling and Result Approximation

Bauer, M.; Aa, Han van der; Weidlich, Matthias

doi:10.1007/978-3-030-26619-6_13

Cited by 19 publications

(16 citation statements)

References 31 publications

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“…As in the simulation method, there is no need to do any alignment computation, it is faster than any other method. For some event logs, the statistical sampling method [20] is not able to provide the approximation faster than the normal conformance checking (i.e., P I < 1). It happens because, this method is not able to benefit from the parallel computing of alignment and after each alignment computation it needs to check if it needs to do more alignment or not.…”

Section: Resultsmentioning

confidence: 99%

“…We also show that using the frequency method is more applicable to select the candidate traces and have accurate results. Results also indicate that although the statistical method [20] is able to approximate accurately, it takes more time and for some event logs, it is slower than the normal conformance checking.…”

Section: Resultsmentioning

confidence: 99%

“…We applied conformance approximation methods with different settings. In this regard, an approximation parameter is We also compared our proposed method with the statistical sampling method [20]. The approximation parameter for this method determines the size and the accuracy of sampling and we consider = δ = approximation parameter × 0.001.…”

Section: Methodsmentioning

confidence: 99%

“…Some research focuses on alignment value approximation. [20] proposes sampling the event log and applying conformance checking on the sampled data. The method increases the sample size until the approximated value is accurate enough.…”

Section: Related Workmentioning

confidence: 99%

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Conformance Checking Approximation Using Subset Selection and Edit Distance

Sani

Zelst

Aalst

2020

Advanced Information Systems Engineering

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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: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Conformance Checking Approximation Using Subset Selection and Edit Distance

Sani

Zelst

Aalst

2020

Advanced Information Systems Engineering

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“…Efficiency improvements have been obtained through the use of search-based methods [34,35], and planning algorithms [36]. Similar to the approximation method presented in Section 5, several approaches approximate conformance results to gain efficiency, e.g., by employing approximate alignments [37], sampling strategies [38], and applying divide-and-conquer schemes in the computation of conformance results [12,39,40]. These approaches can be regarded as complimentary to our approximation method, since our method reduces the number of resolutions for which conformance results need to be obtained, whereas these other approaches improve the efficiency achieved per resolution.…”

Section: Related Workmentioning

confidence: 99%

Partial order resolution of event logs for process conformance checking

Leopold

Weidlich

2020

Decision Support Systems

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While supporting the execution of business processes, information systems record event logs. Conformance checking relies on these logs to analyze whether the recorded behavior of a process conforms to the behavior of a normative specification. A key assumption of existing conformance checking techniques, however, is that all events are associated with timestamps that allow to infer a total order of events per process instance. Unfortunately, this assumption is often violated in practice. Due to synchronization issues, manual event recordings, or data corruption, events are only partially ordered. In this paper, we put forward the problem of partial order resolution of event logs to close this gap. It refers to the construction of a probability distribution over all possible total orders of events of an instance. To cope with the order uncertainty in real-world data, we present several estimators for this task, incorporating different notions of behavioral abstraction. Moreover, to reduce the runtime of conformance checking based on partial order resolution, we introduce an approximation method that comes with a bounded error in terms of accuracy. Our experiments with real-world and synthetic data reveal that our approach improves accuracy over the state-of-the-art considerably.

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