Parallel Distributed Patterns Mining Using Hadoop MapReduce Framework

Abstract

The treatment of large data is proving more difficult in different axes, but the arrival of the framework MapReduce is a solution of this problem. With it we can analyze and process vast amounts of data. It does this by distributing the computational work across a cluster of virtual servers running in a cloud or large set of machines while process mining provides an important bridge between data mining and business process analysis. The process mining techniques allow for extracting information from event logs. In general, there are two steps in process mining: correlation definition or discovery and process inference or composition. Firstly, the authors' work consists to mine small patterns from a log traces. Those patterns are the representation of the traces execution from a log file of a business process. In this step, they 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 MapReduce framework. They have two parties: the first is the Map Step in which they mine patterns from execution traces; the second is the combination of these small patterns as reduce step. The authors' results are promising in that they show that their approach is scalable, general, and precise. It minimizes the execution time by the use of the MapReduce framework.

Earlier stage for straggler detection and handling using combined CPU test and LATE methodology

“…MapReduce can run the applications on a parallel cluster of hardware automatically. In addition; it can process terabytes and petabytes of data more rapidly [3,4]. Recently, it has gained popularity in a wide range of applications due to its ability to provide a highly effective and efficient framework for the parallel execution of the applications, data allocation in distributed database systems, and fault tolerance network communications [5].…”

Enabling Strategies for Big Data Analytics in Hybrid Infrastructures

“…MapReduce (MR) [4] is a programming framework proposed by Google that is currently adopted by many large companies and has been employed as a successful solution for data processing and analysis. The most popular open-source implementation of MR is the Hadoop [5], [6], which is used for deploying Hadoop clusters as the basis for several Big Data analytics.…”