Welcome to the InfoSci Platform
Reference Hub2
Efficient Implementation of Hadoop MapReduce based Business Process Dataflow

Efficient Implementation of Hadoop MapReduce based Business Process Dataflow

Ishak H.A. Meddah, Khaled Belkadi, Mohamed Amine Boudia
Copyright: © 2017 |Volume: 9 |Issue: 1 |Pages: 12
ISSN: 1941-6296|EISSN: 1941-630X|EISBN13: 9781522512554|DOI: 10.4018/IJDSST.2017010104
Cite Article Cite Article

MLA

Meddah, Ishak H.A., et al. "Efficient Implementation of Hadoop MapReduce based Business Process Dataflow." IJDSST vol.9, no.1 2017: pp.49-60. http://doi.org/10.4018/IJDSST.2017010104

APA

Meddah, I. H., Belkadi, K., & Boudia, M. A. (2017). Efficient Implementation of Hadoop MapReduce based Business Process Dataflow. International Journal of Decision Support System Technology (IJDSST), 9(1), 49-60. http://doi.org/10.4018/IJDSST.2017010104

Chicago

Meddah, Ishak H.A., Khaled Belkadi, and Mohamed Amine Boudia. "Efficient Implementation of Hadoop MapReduce based Business Process Dataflow," International Journal of Decision Support System Technology (IJDSST) 9, no.1: 49-60. http://doi.org/10.4018/IJDSST.2017010104

Export Reference

Mendeley
Favorite Full-Issue Download

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.