Active Trace Clustering for Improved Process Discovery

Weerdt, Jochen De; Broucke, Seppe vanden; Vanthienen, Jan; Baesens, Bart

doi:10.1109/tkde.2013.64

Cited by 153 publications

(125 citation statements)

References 22 publications

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“…As duplicated tasks may also manifest themselves as multiple variants of executing a set of activities within the same process, trace clustering was proposed as a way to distinguish these variants [12,13]. However, clustering techniques always consider entire traces and thus also unnecessary duplicate tasks which are the same in all variants.…”

Section: Related Workmentioning

confidence: 99%

Handling Duplicated Tasks in Process Discovery by Refining Event Labels

Fahland

Biggelaar

et al. 2016

Lecture Notes in Computer Science

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Section: Related Workmentioning

confidence: 99%

Handling Duplicated Tasks in Process Discovery by Refining Event Labels

Fahland

Biggelaar

et al. 2016

Lecture Notes in Computer Science

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“…For some types of processes it is also possible to discover hierarchical process models that allow the analysis of a process at different levels of detail (Bose et al 2011). Trace clustering is a technique to decompose the process data of flexible processes with many different process instance variants that share little overlap in behaviour (Weerdt et al 2013). The clustered process instances are used to mine a more limited model with fewer and stronger dependencies between activities.…”

Section: Related Workmentioning

confidence: 99%

“…Techniques like trace clustering and process cubes can be used to partition a dataset in order to compare different process variants (Weerdt et al 2013;Vogelgesang et al 2016;van der Aalst 2013). Generally, with this type of technique each partition results in a separate process model, possibly annotated with performance information, which can then be compared.…”

Section: Related Workmentioning

confidence: 99%

Guided Interaction Exploration and Performance Analysis in Artifact-Centric Process Models

2018

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Artifact-centric process models aim to describe complex processes as a collection of interacting artifacts. Recent development in process mining allow for the discovery of such models. However, the focus is often on the representation of the individual artifacts rather than their interactions. Based on event data, composite state machines representing artifact-centric processes can be discovered automatically. Moreover, the study provides ways of visualising and quantifying interactions among different artifacts. For example, strongly correlated behaviours in different artifacts can be highlighted. Interesting correlations can be subsequently analysed to identify potential causes of process performance issues. The study provides a strategy to explore the interactions and performance differences in this context. The approach has been fully implemented as a ProM plug-in; the CSM Miner provides an interactive artifact-centric process discovery tool focussing on interactions. The approach has been evaluated using real life data, to show that the guided exploration of artifact interactions can successfully identify process performance issues.

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“…Our approach is novel in that clustering is performed by examining properties of transition systems, not log properties, thus being more accurate in determining which clusters will be visually friendly. A related approach is presented in [9], where the quality and simplicity of the models obtained by the heuristic miner is used to determine similarity of traces. Our approach does not depend on any miner during the clustering process.…”

Section: Related Workmentioning

confidence: 99%

Mining structured petri nets for the visualization of process behavior

Pedro

Cortadella

2016

Proceedings of the 31st Annual ACM Symposium on Applied Computing

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Visualization is essential for understanding the models obtained by process mining. Clear and efficient visual representations make the embedded information more accessible and analyzable. This work presents a novel approach for generating process models with structural properties that induce visually friendly layouts. Rather than generating a single model that captures all behaviors, a set of Petri net models is delivered, each one covering a subset of traces of the log. The models are mined by extracting slices of labelled transition systems with specific properties from the complete state space produced by the process logs. In most cases, few Petri nets are sufficient to cover a significant part of the behavior produced by the log.

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Active Trace Clustering for Improved Process Discovery

Cited by 153 publications

References 22 publications

Handling Duplicated Tasks in Process Discovery by Refining Event Labels

Handling Duplicated Tasks in Process Discovery by Refining Event Labels

Guided Interaction Exploration and Performance Analysis in Artifact-Centric Process Models

Mining structured petri nets for the visualization of process behavior

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