NDLI: Record-Aware Two-Level Compression for Big Textual Data Analysis Acceleration

Content Provider	IEEE Xplore Digital Library
Author	Dapeng Dong Herbert, J.
Copyright Year	2015
Description	Author affiliation: Mobile & Internet Syst. Lab. & Insight Centre for Data Analytics, Nat. Univ. of Ireland, Cork, Ireland (Dapeng Dong; Herbert, J.)
Abstract	An increasing volume of data puts MapReduce data analytic platforms such as Hadoop under constant resource pressure. A new two-phase text compression scheme has been specially designed to accelerate data analysis and reduce cluster resource usage, and this has been implemented for Hadoop. The scheme consists of two levels of compression. The first level compression allows a Hadoop program to consume the compressed data directly, thus reducing the data transmission cost within a cluster during analysis. The second level packages data into fixed-size blocks that respect the logical data records. This further reduces the data size to a size similar to that achieved by a higher-order entropy encoder while also making the compressed data splittable for the HDFS. The use of the compression scheme is made transparent to Hadoop developers by the provided utility functions. The compression scheme is evaluated using a set of standard MapReduce jobs for a selection of real-world datasets. The experimental results show an improvement on analysis performance of up to 72% and compression ratios close to that achieved by a standard compressor such as Bzip.
Starting Page	9
Ending Page	16
File Size	235289
Page Count	8
File Format	PDF
ISBN	9781467395601
DOI	10.1109/CloudCom.2015.32
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-30
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Algorithm design and analysis Record-aware Data analysis Compression Content-aware Hadoop Big Data Encoding MapReduce Big data Acceleration Indexing
Content Type	Text
Resource Type	Article

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