Download Data-Driven Process Discovery and Analysis: 4th by Paolo Ceravolo, Barbara Russo, Rafael Accorsi PDF

By Paolo Ceravolo, Barbara Russo, Rafael Accorsi

This ebook constitutes the completely refereed complaints of the Fourth foreign Symposium on Data-Driven method Discovery and research held in Riva del Milan, Italy, in November 2014.

The 5 revised complete papers have been rigorously chosen from 21 submissions. Following the development, authors got the chance to enhance their papers with the insights they received from the symposium. in this variation, the displays and discussions often excited by the implementation of technique mining algorithms in contexts the place the analytical strategy is fed by way of information streams. the chosen papers underline the main proper demanding situations pointed out and suggest novel strategies and techniques for his or her solution.

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Additional info for Data-Driven Process Discovery and Analysis: 4th International Symposium, SIMPDA 2014, Milan, Italy, November 19-21, 2014, Revised Selected Papers

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For example, Fig. 4 shows the submodels that may be discovered from the sublogs of L1 as mentioned above. Finally, the submodels are merged into an overall model (cf. Fig. 3). Any merging algorithm in B(N (A)) → N (A) can be used for this step. Currently, submodels are merged based on activity labels. A. Hompes et al. Fig. 5. The general decomposed process discovery workflow, using a more complex event log L2 as an example. Finding a suitable activity clustering is of key importance. sublogs and submodels, and ||C|| activities in the final, merged model.

Section 3 introduces necessary preliminary definitions for decomposed process mining and the generic decomposition approach. Section 4 introduces decomposition quality notions to grade a decomposition upon, and two approaches that create a high quality decomposition according to those notions. Section 5 shows a (small) use case. The paper is concluded with views on future work in Sect. 6. A. Hompes et al. modeled behavior (resp. the event log and the model). Various discovery algorithms and many different modeling formalisms have been proposed in literature.

Example causal activity graph G1 for causal activity matrix M1 . Definition 7 (Causal Activity Graph). Let A ⊆ UA be a set of activities. G(A) denotes the set of causal activity graphs over A. 0] is a weight function that maps every edge onto a positive weight. 0) iff • E = {(a1 , a2 ) ∈ A × A | M (a1 , a2 ) > τ }, • V = (a1 ,a2 )∈E {a1 , a2 }, and • w((a1 , a2 )) = M (a1 ,a2 )−τ 1−τ for (a1 , a2 ) ∈ E. That is, for every pair of activities (a1 , a2 ) ∈ A, there’s an edge with a positive weight from a1 to a2 in G iff the value for a1 to a2 in the causal activity matrix M exceeds some threshold τ .

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