A Novel Approach for Process Mining Based on Event Types

Ren, Changrui; Wen, Lijie; Dong, Jin; Ding, Hongwei; Wang, Wei; Qiu, Minmin

doi:10.1109/scc.2007.12

Cited by 13 publications

(6 citation statements)

References 3 publications

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“…Here Action = ObservableAction and h is the identity morphism (there are no duplicated events). (Wen et al, 2006) (Ren et al, 2007). They are able to mine models having single tasks only.…”

Section: Process Miningmentioning

confidence: 99%

Skeletal Algorithms

2011

Proceedings of the International Conference on Evolutionary Computation Theory and Applications

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This paper introduces a new kind of evolutionary method, called "skeletal algorithm", and shows its sample application to process mining. The basic idea behind the skeletal algorithm is to express a problem in terms of congruences on a structure, build an initial set of congruences, and improve it by taking limited unions/intersections, until a suitable condition is reached. Skeletal algorithms naturally arise in the context of data/process minig, where the skeleton is the "free" structure on initial data and a congruence corresponds to similarities in data. In such a context, skeletal algorithms come equipped with fitness functions measuring the complexity of a model. We examine two fitness functions for our sample problem-one based on Minimum Description Length Principle, and the other based on Bayesian Interpretation. • an observable action of the event; we shall assume, that we are given only some rough information about the real actions. and we shall forget about any additional information

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“…Here Action = ObservableAction and h is the identity morphism (there are no duplicated events). (Wen et al, 2006) (Ren et al, 2007). They are able to mine models having single tasks only.…”

Section: Process Miningmentioning

confidence: 99%

Skeletal Algorithms

2011

Proceedings of the International Conference on Evolutionary Computation Theory and Applications

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“…Our main tool in process-discovery [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15] was an open source platform ProM [16] with various plugins (i.e. Directly-follows Graph, IVM, Discover Graph).…”

Section: Introductionmentioning

confidence: 99%

Process Mining Methods for Post-Delivery Validation

Markowski

Przybylek

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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“…Another way is by extracting information from a set of events gathered during executions of a process. Process mining (van der Aalst, 2011;Valiant, 1984;Weijters and van der Aalst, 2001;de Medeiros et al, 2004;van der Aalst et al, 2000;van der Aalst et al, 2006b;Wynn et al, 2004;van der Aalst et al, 2006a;van der Aalst and M. Pesic, 2009;van der Aalst and van Dongen, 2002;Wen et al, 2006;Ren et al, 2007) is a growing technology in the context of business process analysis. It aims at extracting this information and using it to build a model.…”

Section: Introductionmentioning

confidence: 99%