Efficient Implementation of Hadoop MapReduce based Business Process Dataflow

Efficient Implementation of Hadoop MapReduce based Business Process Dataflow

Ishak H.A. Meddah (Université USTO, Saida, Algeria), Khaled Belkadi (LAMOSI Laboratory, Mathematics and Computer Science Faculty, USTO-MB University, Oran, Algeria) and Mohamed Amine Boudia (Dr. Moulay Tahar University of Saida, Saida, Algeria)
Copyright: © 2017 |Pages: 12
DOI: 10.4018/IJDSST.2017010104
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Abstract

Hadoop MapReduce is one of the solutions for the process of large and big data, with-it the authors can analyze and process data, it does this by distributing the computational in a large set of machines. Process mining provides an important bridge between data mining and business process analysis, his techniques allow for mining data information from event logs. Firstly, the work consists to mine small patterns from a log traces, those patterns are the workflow of the execution traces of business process. The authors' work is an amelioration of the existing techniques who mine only one general workflow, the workflow present the general traces of two web applications; they use existing techniques; the patterns are represented by finite state automaton; the final model is the combination of only two types of patterns whom are represented by the regular expressions. Secondly, the authors compute these patterns in parallel, and then combine those patterns using MapReduce, they have two parts the first is the Map Step, they mine patterns from execution traces and the second is the combination of these small patterns as reduce step. The results are promising; they show that the approach is scalable, general and precise. It reduces the execution time by the use of Hadoop MapReduce Framework.
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Many techniques are suggested in the domain of process mining, we quote:

Gabel and Su (2008) 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.

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