Understanding Spaghetti Models with Sequence Clustering for ProM

Veiga, Gabriel M.; Ferreira, Diogo R.

doi:10.1007/978-3-642-12186-9_10

Cited by 74 publications

(41 citation statements)

References 10 publications

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“…Clustering techniques can be used as a preprocessing step, and their purpose is to handle event logs that contain large amounts of data and high variability in the recorded behavior [34]. Rather than running controlflow mining techniques directly on large event logs, which would generate very confusing models, by using clustering techniques it is possible to divide traces into clusters, such that similar types of behavior are grouped in the same cluster.…”

Section: Clustering Techniquesmentioning

confidence: 99%

Business process analysis in healthcare environments: A methodology based on process mining

Rebuge

Ferreira

2012

Information Systems

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387

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Performing business process analysis in healthcare organizations is particularly difficult due to the highlydynamic, complex, ad-hoc, and multi-disciplinary nature of healthcare processes. Process mining is a promising approach to obtain a better understanding about those processes by analyzing event data recorded in healthcare information systems. However, not all process mining techniques perform well in capturing the complex and ad-hoc nature of clinical workflows. In this work we introduce a methodology for the application of process mining techniques that leads to the identification of regular behavior, process variants, and exceptional medical cases. The approach is demonstrated in a case study conducted at a hospital emergency service. For this purpose, we implemented the methodology in a tool that integrates the main stages of process analysis. The tool is specific to the case study, but the same methodology can be used in other healthcare environments.

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Section: Clustering Techniquesmentioning

confidence: 99%

Business process analysis in healthcare environments: A methodology based on process mining

Rebuge

Ferreira

2012

Information Systems

Self Cite

387

272

View full text Add to dashboard Cite

show abstract

“…Several approaches to trace clustering have been proposed [2,3,11,4,12,13,5,17]. Some of these techniques produce a flat collection of trace clusters, e.g.…”

Section: Trace Clusteringmentioning

confidence: 99%

“…Some of these techniques produce a flat collection of trace clusters, e.g. [12,17], though most produce hierarchical collections of trace clusters from which models can be mined. Specifically, hierarchical trace clustering methods construct a so-called dendrogram.…”

Section: Trace Clusteringmentioning

confidence: 99%

Controlled automated discovery of collections of business process models

García‐Bañuelos¹,

Dumas²,

Rosa³

et al. 2014

Information Systems

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Automated process discovery techniques aim at extracting process models from information system logs. Existing techniques in this space are effective when applied to relatively small or regular logs, but generate spaghetti-like and sometimes inaccurate models when confronted to logs with high variability. In previous work, trace clustering has been applied in an attempt to reduce the size and complexity of automatically discovered process models. The idea is to split the log into clusters and to discover one model per cluster. This leads to a collection of process models -each one representing a variant of the business process -as opposed to an all-encompassing model. Still, models produced in this way may exhibit unacceptably high complexity and low fitness. In this setting, this paper presents a two-way divide-and-conquer process discovery technique, wherein the discovered process models are split on the one hand by variants and on the other hand hierarchically using subprocess extraction. Splitting is performed in a controlled manner in order to achieve user-defined complexity or fitness thresholds. Experiments on real-life logs show that the technique produces collections of models substantially smaller than those extracted by applying existing trace clustering techniques, while allowing the user to control the fitness of the resulting models.

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“…Also, there has been considerable concern about making control-flow models more understandable [12], but no comparable effort has been done to facilitate the understanding of very large and complex social networks arising from the analysis of real-world event logs.…”

Section: Introductionmentioning

confidence: 99%

Discovering User Communities in Large Event Logs

Ferreira

Alves

2012

Business Process Management Workshops

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Abstract. The organizational perspective of process mining supports the discovery of social networks within organizations by analyzing event logs recorded during process execution. However, applying these social network mining techniques to real data generates very complex models that are hard to analyze and understand. In this work we present an approach to overcome these difficulties by focusing on the discovery of communities from such event logs. The clustering of users into communities allows the analysis and visualization of the social network at different levels of abstraction. The proposed approach also makes use of the concept of modularity, which provides an indication of the best division of the social network into community clusters. The approach was implemented in the ProM framework and it was successfully applied in the analysis of the emergency service of a medium-sized hospital.

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Understanding Spaghetti Models with Sequence Clustering for ProM

Cited by 74 publications

References 10 publications

Business process analysis in healthcare environments: A methodology based on process mining

Business process analysis in healthcare environments: A methodology based on process mining

Controlled automated discovery of collections of business process models

Discovering User Communities in Large Event Logs

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