Slice, Mine and Dice: Complexity-Aware Automated Discovery of Business Process Models

Ekanayake, Chathura C.; Dumas, Marlon; García‐Bañuelos, Luciano; Rosa, Marcello La

doi:10.1007/978-3-642-40176-3_6

Cited by 30 publications

(20 citation statements)

References 20 publications

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“…One can think of subprocesses where this condition does not hold, for example when subprocesses are used not to encapsulate activities pertaining to a business entity (with its own key) but rather to refactor block-structured fragments with loops -without there being a key associated to the loop body -or to refactor fragments shared across multiple process models. Thus, a potential avenue to enhance the technique is to combine it with the two-phase mining approach [12] and shared subprocess extraction techniques as in SMD [6].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Beyond Tasks and Gateways: Discovering BPMN Models with Subprocesses, Boundary Events and Activity Markers

Conforti

Dumas

García‐Bañuelos

et al. 2014

Lecture Notes in Computer Science

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Abstract. Existing techniques for automated discovery of process models from event logs generally produce flat process models. Thus, they fail to exploit the notion of subprocess, as well as error handling and repetition constructs provided by contemporary process modeling notations, such as the Business Process Model and Notation (BPMN). This paper presents a technique for automated discovery of BPMN models containing subprocesses, interrupting and non-interrupting boundary events and activity markers. The technique analyzes dependencies between data attributes attached to events in order to identify subprocesses and to extract their associated logs. Parent process and subprocess models are then discovered using existing techniques for flat process model discovery. Finally, the resulting models and logs are heuristically analyzed in order to identify boundary events and markers. A validation with one synthetic and two real-life logs shows that process models derived using the proposed technique are more accurate and less complex than those derived with flat process discovery techniques.

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Section: Resultsmentioning

confidence: 99%

“…The SMD technique [6] discovers hierarchies of process models related via specialization but also part-of relations. However, SMD only extracts subprocesses that occur in identical or almost identical form in two different specializations of a process.…”

Section: Automated Discovery Of Hierarchical Process Modelsmentioning

confidence: 99%

Beyond Tasks and Gateways: Discovering BPMN Models with Subprocesses, Boundary Events and Activity Markers

Conforti

Dumas

García‐Bañuelos

et al. 2014

Lecture Notes in Computer Science

Self Cite

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show abstract

“…This article is an extended version of a conference paper [29]. With respect to the conference version, the main extension relates to the ability to take into account fitness during the discovery of collections of process models in addition to complexity.…”

Section: Related Workmentioning

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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“…The first group consists of techniques such as presented in [1,2,4,5,9], which basically transform an input event log into a propositional format so as to apply well-known clustering techniques from the data mining domain. The technique presented in [4] is slightly different as the similarity between process instances is determined based on string edit operations, while the recently presented technique in [9] adds a complexity-based procedure to determine the optimal number of clusters based on (approximate) clone detection. The latter group of trace clustering techniques [3,6,7] is different in the sense that they are model-driven by relying either on Markov models or Heuristic nets [7].…”

Section: State Of the Artmentioning

confidence: 99%

“…logs presenting a large amount of distinct process behaviour. In the literature, several trace clustering techniques have been described [1][2][3][4][5][6][7][8][9] that are capable of intelligently splitting up an event log into multiple groups of instances so that process discovery techniques can be applied to subsets of behaviour, with more accurate and comprehensible discovered models as a result. However, the application potential of trace clustering techniques is somewhat hampered by the low level of human comprehension.…”

Section: Introductionmentioning

confidence: 99%

SECPI: Searching for Explanations for Clustered Process Instances

Weerdt

Broucke

2014

Lecture Notes in Computer Science

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Abstract. This paper presents SECPI (Search for Explanations of Clusters of Process Instances), a technique that assists users with understanding a trace clustering solution by finding a minimal set of control-flow characteristics whose absence would prevent a process instance from remaining in its current cluster. As such, the shortcoming of current trace clustering techniques regarding the provision of insight into the computation of a particular partitioning is addressed by learning concise individual rules that clearly explain why a certain instance is part of a cluster.

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Slice, Mine and Dice: Complexity-Aware Automated Discovery of Business Process Models

Cited by 30 publications

References 20 publications

Beyond Tasks and Gateways: Discovering BPMN Models with Subprocesses, Boundary Events and Activity Markers

Beyond Tasks and Gateways: Discovering BPMN Models with Subprocesses, Boundary Events and Activity Markers

Controlled automated discovery of collections of business process models

SECPI: Searching for Explanations for Clustered Process Instances

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