Semi-join computation on distributed file systems using map-reduce-merge model

Hassan, Mohamad Al Hajj; Bamha, Mostafa

doi:10.1145/1774088.1774174

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…Join processing in MapReduce has become a hot research topic in recent years [8,3,16,2,22,30,11]. Many studies have been carried out to evaluate join queries and analyze large datasets in a MapReduce environment.…”

Section: Joins With Mapreducementioning

confidence: 99%

A Theoretical and Experimental Comparison of Filter-Based Equijoins in MapReduce

Phan

d’Orazio

Rigaux

2016

Lecture Notes in Computer Science

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Abstract. MapReduce has become an increasingly popular framework for large-scale data processing. However, complex operations such as joins are quite expensive and require sophisticated techniques. In this paper, we review state-of-the-art strategies for joining several relations in a MapReduce environment and study their extension with filter-based approaches. The general objective of filters is to eliminate non-matching data as early as possible in order to reduce the I/O, communication and CPU costs. We examine the impact of systematically adding filters as early as possible in MapReduce join algorithms, both analytically with cost models and practically with evaluations. The study covers binary joins, multi-way joins and recursive joins, and addresses the case of large inputs that gives rise to the most intricate challenges.

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Section: Joins With Mapreducementioning

confidence: 99%

A Theoretical and Experimental Comparison of Filter-Based Equijoins in MapReduce

Phan

d’Orazio

Rigaux

2016

Lecture Notes in Computer Science

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“…Hassan et. Al in [30] [31] tried to explain the working of Map Reduce by using certain algorithms. The working of Map-Reduce is divided into three main operations.…”

Section: Map-reducementioning

confidence: 99%

Cost Efficient Query Optimization in Mobile Environment

Kasana¹,

Sharma²

2015

IJCA

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Today, we live in the world of internet. With the advancement of technology, the amount of data access has increased too many folds. Internet access now is not only limited to computer devices but can now be easily accessed through mobile devices viz. Smartphones, tablets, PDA's. The internet is now available to every common man, and with its use he fires many queries on servers and uploads or downloads data from the internet. In fact, 90% of the world's data came in existence in the last three-four years, and that too because the internet is readily available to each and every common individual. Of these, much data is being uploaded and queried upon by mobile devices. As the number of devices for Internet access has increased, and so is the number of queries fired by the users on a particular server. The time taken by a query to process totally depends on the complexity involved in joining the tables distributed along the network and finally extracting the desired result out of it. Processing and optimization of various queries in mobile devices involve much join computation among data present at different sites that may be static or mobile which in turn requires much energy consumption. A mobile device has limited energy, so, it must be utilized efficiently. Much research work have been done till now, in the field of mobile computation and making efficient use of energy. However, as the mobile devices possess some asymmetric features, and because of that the old techniques used in distributed databases cannot be applied directly. This paper brings out some methods, to efficiently utilize mobile energy by employing per split semi-join using MapReduce Framework of Hadoop.

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“…MapReduce [8] is an efficient programming model for processing mass volume data. Hadoop is an open sourced platform for cloud computing that is designed for the MapReduce model.…”

Section: Fair Scheduler Cloud Computingmentioning

confidence: 99%