LIProMa: Label-Independent Process Matching

Richter, Florian; Zellner, Ludwig; Azaiz, Imen; Winkel, David; Seidl, Thomas

doi:10.1007/978-3-030-37453-2_16

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…The issue of activity labels has also been studied in process matching areas at the model level [31][32][33][34]. These approaches match two process models from different data sources with the aim to find similar structures and activity labels.…”

Section: Event Log Qualitymentioning

confidence: 99%

A Multi-View Framework to Detect Redundant Activity Labels for More Representative Event Logs in Process Mining

Chen

Tam

et al. 2022

Future Internet

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Process mining aims to gain knowledge of business processes via the discovery of process models from event logs generated by information systems. The insights revealed from process mining heavily rely on the quality of the event logs. Activities extracted from different data sources or the free-text nature within the same system may lead to inconsistent labels. Such inconsistency would then lead to redundancy in activity labels, which refer to labels that have different syntax but share the same behaviours. Redundant activity labels can introduce unnecessary complexities to the event logs. The identification of these labels from data-driven process discovery are difficult and rely heavily on human intervention. Neither existing process discovery algorithms nor event data preprocessing techniques can solve such redundancy efficiently. In this paper, we propose a multi-view approach to automatically detect redundant activity labels by using not only context-aware features such as control–flow relations and attribute values but also semantic features from the event logs. Our evaluation of several publicly available datasets and a real-life case study demonstrate that our approach can efficiently detect redundant activity labels even with low-occurrence frequencies. The proposed approach can add value to the preprocessing step to generate more representative event logs.

show abstract

Section: Event Log Qualitymentioning

confidence: 99%

A Multi-View Framework to Detect Redundant Activity Labels for More Representative Event Logs in Process Mining

Chen

Tam

et al. 2022

Future Internet

View full text Add to dashboard Cite

show abstract

“…One approach is process matching through the model level [2, 7,13,21]. These approaches match two process models from different data sources with the aim to find similar structures and activity labels.…”

Section: Introductionmentioning

confidence: 99%

A Multi-View Framework to Detect Redundant Activity Labels for More Representative Event Logs in Process Mining

Chen,

Lu,

Tam

et al. 2021

Preprint

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The insights revealed from process mining heavily rely on the quality of event logs. Activities extracted from different data sources or the free-text nature within the same system may lead to inconsistent labels. Such inconsistency would then lead to redundancy of activity labels, which refer to labels that have different syntax but share the same behaviours. The identifications of these labels from data-driven process discovery are difficult and would rely heavily on human intervention. In this paper, we propose an approach to detect redundant activity labels using control-flow relations and data values from event logs. We have evaluated our approach using two publicly available logs and also a case study using the MIMIC-III data set. The results demonstrate that our approach can detect redundant activity labels even with low occurrence frequencies. This approach can value-add to the preprocessing step to generate more representative event logs for process mining tasks.

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TADE: Stochastic Conformance Checking Using Temporal Activity Density Estimation

Richter

Sontheim

Zellner

et al. 2020

Lecture Notes in Computer Science

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LIProMa: Label-Independent Process Matching

Cited by 3 publications

References 13 publications

A Multi-View Framework to Detect Redundant Activity Labels for More Representative Event Logs in Process Mining

A Multi-View Framework to Detect Redundant Activity Labels for More Representative Event Logs in Process Mining

A Multi-View Framework to Detect Redundant Activity Labels for More Representative Event Logs in Process Mining

TADE: Stochastic Conformance Checking Using Temporal Activity Density Estimation

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