Discovering Patterns using Process Mining

Discovering Patterns using Process Mining

Ishak Meddah (LAMOSI Laboratory, USTO-MB University, Oran, Algeria) and Belkadi Khaled (LAMOSI Laboratory, USTO-MB University, Oran, Algeria)
Copyright: © 2016 |Pages: 11
DOI: 10.4018/IJRSDA.2016100102
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Abstract

Process mining provides an important bridge between data mining and business process analysis, his techniques allow for extracting information from event logs. In general, there are two steps in process mining, correlation definition or discovery and then process inference or composition. Firstly, the authors' work consists to mine small patterns from a log traces of two applications; SKYPE, and VIBER, those patterns are the representation of the execution traces of a business process. In this step, the authors use existing techniques; The patterns are represented by finite state automaton or their regular expression; The final model is the combination of only two types of small patterns whom are represented by the regular expressions (ab)* and (ab*c)*. Secondly, the authors compute these patterns in parallel, and then combine those small patterns using the composition rules, they have two parties the first is the mine, they discover patterns from execution traces and the second is the combination of these small patterns. The patterns mining and the composition is illustrated by the automaton existing techniques. The Execution traces are the different actions effected by users in the SKYPE and VIBER. The results are general and precise. It minimizes the execution time and the loss of information.
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Many techniques are suggested in the domain of process mining, we quote:

Gabel et al. (Gabel & Su, 2008a) present a new general technique for mining temporal specification, they realized their work in two steps; firstly they discovered the simple patterns using existing techniques, then combine these patterns using the composition and some rules like Branching and Sequencing rules.

Temporal specification expresses formal correctness requirement of an application’s ordering of specific actions and events during execution, they discovered patterns from traces of execution or program source code; The simples patterns are represented using regular expression (ab)* or (ab*c)* and their representation using finite state automaton, after they combine simple patterns to construct a temporal specification using a finite state automaton.

Greco et al (Greco et al., 2006) discovered several clusters by using a clustering technique, and then they calculate the pattern from each cluster, they combine these patterns to construct a final model, they discovered a workflow scheme from, and then they mine a workflow using a MineWorkflow Algorithm, after they define many clusters from a log traces by using clustering technique and Process Discover Algorithm and some rules cluster.

Then they use a Find Features Algorithm to find a patterns of each cluster, finally they combine these patterns to construct a completely hierarchical workflow model.

In their clustering algorithm, clusters reflect only structural similarities among traces; they say that in future works extending their techniques to take care of the environment so that clusters may reflect not only structural similarities among traces, but also information about, e.g., users and data values.

Motahari-Nezhed et al. (Motahari-Nezhad, et al., 2008) use a service conversation log; first they split a log into several partitions, 2nd they discovered a model from each partition, and 3rd, they annotate the discover protocol model with various metadata to construct a protocol model from real-word service conversation logs.

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