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20162024
most citedEvent Abstraction for Process Mining using Supervised Learning Techniques

98 citations · 338 across the 9 of their papers we have counts for

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Showing 2016Show all

5 papers · 1 filter

cs.LG2016★ 18 cited

Heuristic Approaches for Generating Local Process Models through Log Projections

Niek Tax, Natalia Sidorova, Wil M. P. van der Aalst +1

Local Process Model (LPM) discovery is focused on the mining of a set of process models where each model describes the behavior represented in the event log only partially, i.e. su…

stat.ME2016★ 6 cited

On Generation of Time-based Label Refinements

Niek Tax, Emin Alasgarov, Natalia Sidorova +1

Process mining is a research field focused on the analysis of event data with the aim of extracting insights in processes. Applying process mining techniques on data from smart hom…

cs.LG2016★ 98 cited

Event Abstraction for Process Mining using Supervised Learning Techniques

Niek Tax, Natalia Sidorova, Reinder Haakma +1

Process mining techniques focus on extracting insight in processes from event logs. In many cases, events recorded in the event log are too fine-grained, causing process discovery…

cs.DB2016★ 7 cited

Log-based Evaluation of Label Splits for Process Models

Niek Tax, Natalia Sidorova, Reinder Haakma +1

Process mining techniques aim to extract insights in processes from event logs. One of the challenges in process mining is identifying interesting and meaningful event labels that…

cs.DB2016★ 96 cited

Mining Local Process Models

Niek Tax, Natalia Sidorova, Reinder Haakma +1

In this paper we describe a method to discover frequent behavioral patterns in event logs. We express these patterns as \emph{local process models}. Local process model mining can…