NDLI: Data analyzing using Map-Join-Reduce in cloud storage

Content Provider	IEEE Xplore Digital Library
Author	Bhardwaj, Ruchi Mishra, Neetesh Kumar, Rajiv
Copyright Year	2014
Description	Author affiliation: Department of Computer Science and Engineering, Sharda University, Greater Noida, India (Bhardwaj, Ruchi; Mishra, Neetesh; Kumar, Rajiv)
Abstract	Data analysis and maintenance in cloud computing is a challenging task which allows large volume of data to be processed in large clusters. Recent days Map Reduce Model have shown great value in processing huge amount of data on very large clusters. Map Reduce paradigm consists of two phases, mapper and reducer. Mapper performs filtering criteria and Reducer performs aggregation task, but Map Reduce supports a homogenous data set that signifies the same filtering logic is applied by mapper function on each tuple in the data set. However these techniques do not performed well in case of complex data analysis that may require the joining of multiple data sets. In order to improve these problems a CloudView framework has been proposed for data storage, processing and analyzing the massive machine data which are collected from cloud environment in which Case Based Reasoning (CBR) approach is used for fault prediction. In this paper, an Enhanced CloudView (ECV) framework has been proposed for data processing, maintenance and analyzing the massive machine data. CloudView is formulated by Map Reduce model whereas ECV framework will use Map-Join-Reduce model. This model will performs mapping-join-reduction task in two successive Map Reduce jobs. First it will filter the logic to all the datasets in parallel, joins the resulted tuple and further reduces for final aggregation and finally, it combines all partial aggregation results and produce the final result. This additional joiner model will perform a fast processing in a heterogeneous data set by using join reduce function, which will improve the efficiency and scalability of the system.
Starting Page	370
Ending Page	373
File Size	281241
Page Count	4
File Format	PDF
ISBN	9781479976829
e-ISBN	9781479976836
DOI	10.1109/PDGC.2014.7030773
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-11
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cloud computing Cloudview Barium Three-dimensional displays Map join reduce Conferences Bismuth Grid computing Extended cloudview Map reduce
Content Type	Text
Resource Type	Article

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