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2020 | OriginalPaper | Buchkapitel

Stochastic-Aware Conformance Checking: An Entropy-Based Approach

verfasst von : Sander J. J. Leemans, Artem Polyvyanyy

Erschienen in: Advanced Information Systems Engineering

Verlag: Springer International Publishing

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Abstract

Business process management (BPM) aims to support changes and innovations in organizations’ processes. Process mining complements BPM with methods, techniques, and tools that provide insights based on observed executions of business processes recorded in event logs of information systems. State-of-the-art discovery and conformance techniques completely ignore or only implicitly consider the information about the likelihood of processes, which is readily available in event logs, even though such stochastic information is necessary for simulation, prediction and recommendation in models. Furthermore, stochastic information can provide business analysts with further actionable insights on frequent and rare conformance issues.

In this paper, we propose precision and recall conformance measures based on the notion of entropy of stochastic automata that are capable of quantifying, and thus differentiating, frequent and rare deviations between an event log and a process model. The feasibility of using the proposed precision and recall measures in industrial settings is demonstrated by an evaluation over several real-world datasets supported by our open-source implementation.

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Vorheriges Kapitel State Machine Based Human-Bot Conversation Model and Services

Nächstes Kapitel Conformance Checking Approximation Using Subset Selection and Edit Distance

For non-stochastic DFAs, a projection is often called a conjunction. We do not use this term here to avoid confusion with the “stochastic” conjunction of two SDFAs, as this “stochastic” conjunction may not necessarily yield an SDFA again.

The event logs are accessible via https://data.4tu.nl/repository/collection:event_logs_real.

The source code of the discovery technique is accessible via https://svn.win.tue.nl/repos/prom/Packages/StochasticPetriNets/Trunk (svn revision 39823).

The source code used in the evaluation is accessible via https://svn.win.tue.nl/repos/prom/Packages/StochasticAwareConformanceChecking/Trunk (revision 41855).

The models are not publicly available, as per the terms of the agreement with the company.

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Titel: Stochastic-Aware Conformance Checking: An Entropy-Based Approach
verfasst von: Sander J. J. Leemans
Artem Polyvyanyy
Verlag: Springer International Publishing
Buch: Advanced Information Systems Engineering
Print ISBN: 978-3-030-49434-6

Electronic ISBN: 978-3-030-49435-3

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-49435-3_14

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