Comparative Study of Multi-query Optimization Techniques using Shared Predicate-based for Big Data

被引:10
作者
Sahal, Radhya [1 ]
Khafagy, Mohamed H. [2 ]
Omara, Fatma A. [1 ]
机构
[1] Cairo Univ, Fac Comp & Informat, Dept Comp Sci, Cairo, Egypt
[2] Fayoum Univ, Fac Comp & Informat, Dept Comp Sci, Faiyum, Egypt
来源
INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING | 2016年 / 9卷 / 05期
关键词
Big data; MapReduce; Sharing Opportunity; Multi-Query Optimization; filter; predicates;
D O I
10.14257/ijgdc.2016.9.5.20
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
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.
引用
收藏
页码:229 / 240
页数:12
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