Conformance checking over uncertain event data

Pegoraro, Marco; Uysal, Merih Seran; Aalst, Wil M. P. van der

doi:10.1016/j.is.2021.101810

Cited by 30 publications

(10 citation statements)

References 15 publications

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“…Pegoraro et al [26] advocate to explicitly encode the uncertainty about events and traces in order to leverage it in a transparent manner during the analysis. Based on this event log with explicit uncertainty representation conformance checking techniques can be adaped [28] to an obtain more trustworthy diagnostics that also provide more transparency about the possible different scenarios compatible with the (uncertain) observations.…”

Section: Data 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: Data Qualitymentioning

confidence: 99%

Responsible Process Mining

Mannhardt

2022

Lecture Notes in Business Information Processing

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“…The problem of event-case correlation can be positioned in the broader context of uncertain event data [15,16]. This research direction aims to analyze event data with imprecise attributes, where single traces might correspond to an array of possible real-life scenarios.…”

Section: Related Workmentioning

confidence: 99%

Uncertain Case Identifiers in Process Mining: A User Study of the Event-Case Correlation Problem on Click Data

Pegoraro¹,

Uysal²,

Hülsmann³

et al. 2022

Preprint

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Among the many sources of event data available today, a prominent one is user interaction data. User activity may be recorded during the use of an application or website, resulting in a type of user interaction data often called click data. An obstacle to the analysis of click data using process mining is the lack of a case identifier in the data. In this paper, we show a case and user study for event-case correlation on click data, in the context of user interaction events from a mobility sharing company. To reconstruct the case notion of the process, we apply a novel method to aggregate user interaction data in separate user sessions-interpreted as cases-based on neural networks. To validate our findings, we qualitatively discuss the impact of process mining analyses on the resulting well-formed event log through interviews with process experts.

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“…It is possible to find bounds for the conformance score of an uncertain trace and a reference process model with an extension of the alignment technique [12]. In order to find such bounds, it is necessary to build a Petri net able to simulate all possible behaviors in the uncertain trace, called the behavior net [11].…”

Section: Conformance Checkingmentioning

confidence: 99%

Probabilistic and Non-Deterministic Event Data in Process Mining: Embedding Uncertainty in Process Analysis Techniques

Pegoraro¹

2022

Preprint

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Process mining is a subfield of process science that analyzes event data collected in databases called event logs. Recently, novel types of event data have become of interest due to the wide industrial application of process mining analyses. In this paper, we examine uncertain event data. Such data contain meta-attributes describing the amount of imprecision tied with attributes recorded in an event log. We provide examples of uncertain event data, present the state of the art in regard of uncertainty in process mining, and illustrate open challenges related to this research direction.

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Conformance checking over uncertain event data

Cited by 30 publications

References 15 publications

Responsible Process Mining

Responsible Process Mining

Uncertain Case Identifiers in Process Mining: A User Study of the Event-Case Correlation Problem on Click Data

Probabilistic and Non-Deterministic Event Data in Process Mining: Embedding Uncertainty in Process Analysis Techniques

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