Earticle

초록

영어

Big data analytical systems, such as MapReduce, have become main issues for many enterprises and research groups. Currently, multi-query which translated into MapReduce jobs is submitted repeatedly with similar tasks. So, exploiting these similar tasks can offer possibilities to avoid repeated computations of MapReduce jobs. Therefore, many researches have addressed the sharing opportunity to optimize multi-query processing. Consequently, the main goal of this work is to study and compare comprehensively two existed sharing opportunity techniques using predicate-based filters; MRShare and relaxed MRShare. The comparative study has been performed over TPC-H benchmark and confirmed that the relaxed MRShare technique significantly outperforms the MRShare for shared data in terms of predicate-based filters among multi-query.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Multi-Query Optimization in MapReduce
  3.1 Overview of Multi-Query Optimization
  3.2 MapReduce Query Processing
  3.3 Difference between Multi-Query Optimization on DBMS and Map Reduce
 4. Comparative Techniques of Multi-Query Optimization on MapReduce Environment
  4.1 MRShare
  4.2 Relaxed MRShare
 5. Predicate-Based Filters on Shared Data
  5.1 Predicate-Based Filters
  5.2 Example of Comparative Techniques
 6. Analysis of the Comparative Techniques
  6.1 Experiment Setup
  6.2 Datasets and Queries
  6.3 Performance Evaluation
 7. Conclusions
 References

키워드

저자

• Radhya Sahal [ Department of Computer Science, Faculty of Computers & Information, Cairo University, Egypt ]
• Mohamed H. Khafagy [ Department of Computer Science, Faculty of Computers & Information, Fayoum University, Egypt ]
• Fatma A. Omara [ Department of Computer Science, Faculty of Computers & Information, Cairo University, Egypt ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605