Data-Driven Process Discovery and Analysis: Second IFIP WG by Marcello Leida, Basim Majeed (auth.), Philippe PDF

By Marcello Leida, Basim Majeed (auth.), Philippe Cudre-Mauroux, Paolo Ceravolo, Dragan Gašević (eds.)

ISBN-10: 3642409180

ISBN-13: 9783642409189

ISBN-10: 3642409199

ISBN-13: 9783642409196

This publication constitutes the completely refereed court cases of the second one overseas Symposium on Data-Driven method Discovery and research held in Campione d'Italia, Italy, in June 2012.

The six revised complete papers have been conscientiously chosen from 17 submissions. to enhance the standard of the contributions the symposium fostered the dialogue in the course of the presentation, giving authors the chance to enhance their paintings extending the awarded effects. the chosen papers conceal themes spanning from theoretical concerns on the topic of approach illustration, discovery and research to functional and operational reports in approach discovery and analysis.

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To explain our approach, it is useful to reflect on the four basic quality dimensions of the process model with respect to the observed behavior [1,2] (cf. Figure 1a). The replay fitness dimension quantifies the extent to which the discovered model can accurately replay the cases recorded in the log. The precision dimension measures whether the discovered model prohibits behavior which is not seen in the event log. The generalization dimension assesses the extent to which the resulting model will be able to reproduce possible future, yet unseen, behavior of the process.

Al. [11]. t. the unsound process model, whereby ‘badness’ indicates the ability of a solution to produce traces that lead to unsound behavior. The approach uses simulated annealing to simultaneously minimize all three dimensions. The edits applied to the process model are aimed to correct the model rather than to balance the five different forces. Detecting deviations of a process model from the observed behavior has been researched, among others, by Adriansyah et. al. [2,4]. Given a process model and an event log, deviations are expressed in the form of skipped activities (activities that should be performed according to the model, but do not occur in the log) and inserted activities (activities that are not supposed to happen according to the model, but that occur in the log).

By incorporating this dimension, we can present a discovered model that maximizes the four dimensions while remaining aligned, as far as possible, with the intuitions and familiar notions modeled in a reference model. “able to replay event log” “not overfitting the log” “Occam’s razor” “not underfitting the log” (a) Different quality dimensions for Process Model Discovery [1] (b) Incorporating similarity Fig. 1. M. Buijs et al. Figure 1b illustrates the effects of introducing this additional dimension.

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Data-Driven Process Discovery and Analysis: Second IFIP WG 2.6, 2.12 International Symposium, SIMPDA 2012, Campione d’Italia, Italy, June 18-20, 2012, Revised Selected Papers by Marcello Leida, Basim Majeed (auth.), Philippe Cudre-Mauroux, Paolo Ceravolo, Dragan Gašević (eds.)


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