Parallel and Distributed Pattern Mining

Parallel and Distributed Pattern Mining

Ishak H.A Meddah (Labri Laboratory, École Supérieure en Informatique, Sidi Bel Abbés, Algeria) and Nour El Houda REMIL (Mascara University “Mustapha STAMBOULI”, Mascara, Algeria)
Copyright: © 2019 |Pages: 17
DOI: 10.4018/IJRSDA.2019070101

Abstract

The treatment of large data is difficult and it looks like the arrival of the framework MapReduce is a solution of this problem. This framework can be used to analyze and process vast amounts of data. This happens by distributing the computational work across a cluster of virtual servers running in a cloud or a large set of machines. Process mining provides an important bridge between data mining and business process analysis. Its techniques allow for extracting information from event logs. Generally, there are two steps in process mining, correlation definition or discovery and the inference or composition. First of all, their work mines small patterns from log traces. Those patterns are the representation of the traces execution from a log file of a business process. In this step, the authors use existing techniques. The patterns are represented by finite state automaton or their regular expression; and the final model is the combination of only two types of different patterns whom are represented by the regular expressions (ab)* and (ab*c)*. Second, they compute these patterns in parallel, and then combine those small patterns using the Hadoop framework. They have two steps; the first is the Map Step through which they mine patterns from execution traces, and the second one is the combination of these small patterns as a reduce step. The results show that their approach is scalable, general and precise. It minimizes the execution time by the use of the Hadoop framework.
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Many techniques are suggested in the domain of process mining; we quote:

M. Gabel et al. (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.

G.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 mine workflow 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 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.

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