Content-aware partial compression for textual big data analysis in Hadoop
| dc.contributor.author | Dong, Dapeng | |
| dc.contributor.author | Herbert, John | |
| dc.date.accessioned | 2018-02-13T12:29:30Z | |
| dc.date.available | 2018-02-13T12:29:30Z | |
| dc.date.issued | 2017-06-29 | |
| dc.date.updated | 2018-02-13T12:23:29Z | |
| dc.description.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. | en |
| dc.description.status | Peer reviewed | en |
| dc.description.version | Accepted Version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Dong, D. and Herbert, J. (2017) 'Content-aware Partial Compression for Textual Big Data Analysis in Hadoop', IEEE Transactions on Big Data,4(4), pp.459-472 doi: 10.1109/TBDATA.2017.2721431 | en |
| dc.identifier.doi | 10.1109/TBDATA.2017.2721431 | |
| dc.identifier.endpage | 472 | en |
| dc.identifier.issn | 2332-7790 | |
| dc.identifier.issued | 4 | |
| dc.identifier.journaltitle | IEEE Transactions on Big Data | en |
| dc.identifier.startpage | 459 | en |
| dc.identifier.uri | https://hdl.handle.net/10468/5452 | |
| dc.identifier.volume | 4 | |
| dc.language.iso | en | en |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en |
| dc.rights | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en |
| dc.subject | Algorithm design and analysis | en |
| dc.subject | Big Data | en |
| dc.subject | Data analysis | en |
| dc.subject | Data compression | en |
| dc.subject | Distributed databases | en |
| dc.subject | Organizations | en |
| dc.subject | Servers | en |
| dc.subject | Compression | en |
| dc.subject | Distributed File System | en |
| dc.subject | MapReduce | en |
| dc.title | Content-aware partial compression for textual big data analysis in Hadoop | en |
| dc.type | Article (peer-reviewed) | en |
