All that Glitters Is Not Gold

Werf, Jan Martijn E. M. van der; Polyvyanyy, Artem; Wensveen, Bart R. van; Brinkhuis, Matthieu; Reijers, Hajo A.

doi:10.1007/978-3-030-79382-1_9

Cited by 5 publications

(1 citation statement)

References 26 publications

(49 reference statements)

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“…A somewhat related issue on the confidence an analyst can put in the performance of a process discovery algorithm was brought up by Van der Werf et al [37]. They observed that process discovery techniques not always discover better process models when provided with a better sample of the process behavior, i.e., a larger event log with observations of process behavior.…”

Section: Model Qualitymentioning

confidence: 99%

Responsible Process Mining

Mannhardt

2022

Lecture Notes in Business Information Processing

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The prospect of data misuse negatively affecting our life has lead to the concept of responsible data science. It advocates for responsibility to be built, by design, into data management, data analysis, and algorithmic decision making techniques such that it is made difficult or even impossible to intentionally or unintentionally cause harm. Process mining techniques are no exception to this and may be misused and lead to harm. Decisions based on process mining may lead to unfair decisions causing harm to people by amplifying the biases encoded in the data by disregarding infrequently observed or minority cases. Insights obtained may lead to inaccurate conclusions due to failing to considering the quality of the input event data. Confidential or personal information on process stakeholders may be leaked as the precise work behavior of an employee can be revealed. Process mining models are usually white-box but may still be difficult to interpret correctly without expert knowledge hampering the transparency of the analysis. This chapter structures the topic of responsible process mining based on the FACT criteria: Fairness, Accuracy, Confidentiality, and Transparency. For each criteria challenges specific to process mining are provided and the current state of the art is briefly summarized.

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Section: Model Qualitymentioning

confidence: 99%

Responsible Process Mining

Mannhardt

2022

Lecture Notes in Business Information Processing

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Bootstrapping Generalization of Process Models Discovered from Event Data

Polyvyanyy

Moffat

García‐Bañuelos

2022

Advanced Information Systems Engineering

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Partially ordered stochastic conformance checking

Leemans,

Brockhoff,

van der Aalst

et al. 2024

Knowl Inf Syst

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Process mining aids organisations in improving their operational processes by providing visualisations and algorithms that turn event data into insights. How often behaviour occurs in a process—the stochastic perspective—is important for simulation, recommendation, enhancement and other types of analysis. Although the stochastic perspective is important, the focus is often on control flow. Stochastic conformance checking techniques assess the quality of stochastic process models and/or event logs with one another. In this paper, we address three limitations of existing stochastic conformance checking techniques: inability to handle uncertain event data (e.g. events having only a date), exponential blow-up in computation time due to the analysis of all interleavings of concurrent behaviour and the problem that loops that can be unfolded infinitely often. To address these challenges, we provide bounds for conformance measures and use partial orders to encode behaviour. An open-source implementation is provided, which we use to illustrate and evaluate the practical feasibility of the approach.

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All that Glitters Is Not Gold

Cited by 5 publications

References 26 publications

Responsible Process Mining

Responsible Process Mining

Bootstrapping Generalization of Process Models Discovered from Event Data

Partially ordered stochastic conformance checking

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