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

By Paolo Ceravolo, Barbara Russo, Rafael Accorsi

This e-book constitutes the completely refereed lawsuits of the Fourth overseas Symposium on Data-Driven approach 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 won from the symposium. in this variation, the shows and discussions often fascinated by the implementation of strategy mining algorithms in contexts the place the analytical technique is fed via information streams. the chosen papers underline the main suitable demanding situations pointed out and suggest novel ideas and methods 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

Example text

For the remainder of this paper, we let UA be some universe of activities. Definition 5 (Event log). Let A ⊆ UA be a set of activities. A trace s ∈ S(A) is a sequence of activities. Let L ∈ B(S(A)) be a multiset of traces over A. L is an event log over A. An example event log is L1 = [ a, b, c, d 5 , a, b, b, c, d 2 , a, c, d 3 ]. There are three unique traces in L1 , and it contains information about a total of 10 cases. There are 4·5+5·2+3·3 = 39 events in total. The projection can be used for event logs as well.

Let A be a set and Q ⊆ A a subset. Q ∈ S(A) → S(Q) is a projection function and is defined recursively: (1) Q = and (2) for s ∈ S(A) and a ∈ A: ( a · s) So a, a, b, b, c, d, d {a,b} Q = = a, a, b, b . A. Hompes et al. Event Logs Event logs are the starting point for process mining. They contain information recorded by the information systems and resources supporting a process. Typically, the executed activities of multiple cases of a process are recorded. e. event logs only contain information that has been seen.

Commun. A. W. P. nl Abstract. Event data can be found in any information system and provide the starting point for a range of process mining techniques. The widespread availability of large amounts of event data also creates new challenges. Existing process mining techniques are often unable to handle “big event data” adequately. Decomposed process mining aims to solve this problem by decomposing the process mining problem into many smaller problems which can be solved in less time, using less resources, or even in parallel.

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