Repairing Event Logs Using Timed Process Models

Solti, Andreas; Mans, Ronny S.; Aalst, Wil M. P. van der; Weske, Mathias

doi:10.1007/978-3-642-41033-8_89

Cited by 29 publications

(20 citation statements)

References 8 publications

(4 reference statements)

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“…For example, # H time (ê 11 ) = # time (e 20 ) = 185 min. In the second case, there are multiple possible methods to determine the most likely timestamp for a model move (e.g., based on statistical methods [16]). Therefore, we abstract from the concrete implementation with the method obtainTime.…”

Section: Abstraction Algorithmmentioning

confidence: 99%

Guided Process Discovery – A pattern-based approach

Mannhardt

Leoni

Reijers

et al. 2018

Information Systems

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Section: Abstraction Algorithmmentioning

confidence: 99%

Guided Process Discovery – A pattern-based approach

Mannhardt

Leoni

Reijers

et al. 2018

Information Systems

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“…В силу своей XML-природы этот формат позволяет исправлять журналы собы-тий путем отфильтровывания испорченных трасс. Более подробно об исправлении журналов событий можно узнать из [16]. Очевидно, что нет смысла рассматривать сильно испорченные журналы событий.…”

Section: классификация шумов в журналах событийunclassified

On process model synthesis based on event logs with noise

Mitsyuk

Shugurov

2016

Aut. Control Comp. Sci.

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“…To the best of our knowledge, this is the first study aiming at systematically defining the effect of noise on mined models. In fact, Rogge-Solti et al [37][38][39] have tackled the challenge of repairing logs on the basis of statistical information derived from correct logs and imperative process models. In their study, the process model is known a priori, and the objective is to derive a reliable log from one containing missing or incorrect information.…”

Section: Related Workmentioning

confidence: 99%

The Effect of Noise on Mined Declarative Constraints

Ciccio

Mecella

Mendling

2015

Lecture Notes in Business Information Processing

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Declarative models are increasingly utilized as representational format in process mining. Models created from automatic process discovery are meant to summarize complex behaviors in a compact way. Therefore, declarative models do not define all permissible behavior directly, but instead define constraints that must be met by each trace of the business process. While declarative models provide compactness, it is up until now not clear how robust or sensitive different constraints are with respect to noise. In this paper, we investigate this question from two angles. First, we establish a constraint hierarchy based on formal relationships between the different types of Declare constraints. Second, we conduct a sensitivity analysis to investigate the effect of noise on different types of declarative rules. Our analysis reveals that an increasing degree of noise reduces support of many constraints. However, this effect is moderate on most of the constraint types, which supports the suitability of Declare for mining event logs with noise.

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Repairing Event Logs Using Timed Process Models

Cited by 29 publications

References 8 publications

Guided Process Discovery – A pattern-based approach

Guided Process Discovery – A pattern-based approach

On process model synthesis based on event logs with noise

The Effect of Noise on Mined Declarative Constraints

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