2021
DOI: 10.33166/aetic.2021.04.001
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Process Discovery Enhancement with Trace Clustering and Profiling

Abstract: The potential in process mining is progressively growing due to the increasing amount of event-data. Process mining strategies use event-logs to automatically classify process models, recommend improvements, predict processing times, check conformance, and recognize anomalies/deviations and bottlenecks. However, proper handling of event-logs while evaluating and using them as input is crucial to any process mining technique. When process mining techniques are applied to flexible systems with a large number of… Show more

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Cited by 2 publications
(4 citation statements)
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“…In this section, we present the results obtained by application of the proposed framework for trace clustering. We used two baseline trace clustering techniques Jablonski et al [20] and Faizan et al [24] for the experimental evaluation of our approach and refer to them as baseline 1 and baseline 2 throughout this section.…”
Section: Resultsmentioning
confidence: 99%
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“…In this section, we present the results obtained by application of the proposed framework for trace clustering. We used two baseline trace clustering techniques Jablonski et al [20] and Faizan et al [24] for the experimental evaluation of our approach and refer to them as baseline 1 and baseline 2 throughout this section.…”
Section: Resultsmentioning
confidence: 99%
“…In the context of clustering the traces based on trace features, researchers in [50], [51], [20], [24], and [52] proposed to use several distinct event log features to group similar traces. The main idea behind trace-features-based clustering was to project the event log into a vector space representation called trace profiles.…”
Section: Related Workmentioning
confidence: 99%
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