Dong, Dapeng (2018) Content-aware Partial Compression for Textual Big Data Analysis in Hadoop. IEEE TRANSACTIONS ON BIG DATA, 4 (4). pp. 459-472. ISSN 2332-7790

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Abstract

A substantial amount of information in companies and on the Internet is present in the form of text. The value of this semi-structured and unstructured data has been widely acknowledged, with consequent scientific and commercial exploitation. The ever-increasing data production, however, pushes data analytic platforms to their limit. Compression as an effective means to reduce data size has been employed by many emerging data analytic platforms, whom the main purpose of data compression is to save storage space and reduce data transmission cost over the network. Since general purpose compression methods endeavour to achieve higher compression ratios by leveraging data transformation techniques and contextual data, this context-dependency forces the access to the compressed data to be sequential. Processing such compressed data in parallel, such as desirable in a distributed environment, is extremely challenging. This work proposes techniques for more efficient textual big data analysis with an emphasis on content-aware compression schemes suitable for the Hadoop analytic platform. The compression schemes have been evaluated for a number of standard MapReduce analysis tasks using a collection of public and private real-world datasets. In comparison with existing solutions, they have shown substantial improvement in performance and significant reduction in system resource requirements.

Item Type:	Article
Additional Information:	This is the preprint version of the published article, which s available at D. Dong and J. Herbert, "Content-Aware Partial Compression for Textual Big Data Analysis in Hadoop," in IEEE Transactions on Big Data, vol. 4, no. 4, pp. 459-472, 1 Dec. 2018, doi: 10.1109/TBDATA.2017.2721431.
Keywords:	Big Data; Compression; MapReduce; Distributed File System;
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	13168
Identification Number:	https://doi.org/10.1109/TBDATA.2017.2721431
Depositing User:	Dapeng Dong
Date Deposited:	05 Aug 2020 15:46
Journal or Publication Title:	IEEE TRANSACTIONS ON BIG DATA
Publisher:	IEEE
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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