Indexing strategies of MapReduce for information retrieval in big data

In Information Retrieval (IR) the efficient strategy of indexing large dataset and terabyte-scale data is still an issue because of information overload as the result of increasing the knowledge, increasing the number of different media, increasing the number of platforms, and increasing the i...

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Main Author:	Ramadhan, Mazen Farid Ebrahim
Format:	Thesis
Language:	English
Published:	2016
Subjects:	Big data Information retrieval MapReduce (Computer file)
Online Access:	http://psasir.upm.edu.my/id/eprint/66723/1/FSKTM%202016%2025%20IR.pdf
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id	my-upm-ir.66723
record_format	uketd_dc
spelling	my-upm-ir.667232019-01-31T02:28:06Z Indexing strategies of MapReduce for information retrieval in big data 2016-01 Ramadhan, Mazen Farid Ebrahim In Information Retrieval (IR) the efficient strategy of indexing large dataset and terabyte-scale data is still an issue because of information overload as the result of increasing the knowledge, increasing the number of different media, increasing the number of platforms, and increasing the interoperability of platforms. Overall multiple processing machines MapReduce has been suggested as a suitable platform that use for distributing the intensive data operations. In this project, Sensei and Per-posting list indexing, Terrier will be analysed as they are the two most efficient MapReduce indexing strategies. The two indexing will be implemented in an existing framework of IR, and an experiment will be performed by using the Hadoop for MapReducing with the same large dataset, and try to analyse and verify the better efficient strategy between Sensei and Terrier. The experiment will measure the performance of retrieving when the size and processing power enlarge. The experiment examines how the indexing strategies scaled and work with large size of dataset and distributed number of different machines. The throughput will be measured by using MB/S (megabyte/per second), and the experiment results analyzing the performance of delay, consuming time and efficiency of indexing strategies between Sensei and Per-posting list indexing ,Terrier. Big data Information retrieval MapReduce (Computer file) 2016-01 Thesis http://psasir.upm.edu.my/id/eprint/66723/ http://psasir.upm.edu.my/id/eprint/66723/1/FSKTM%202016%2025%20IR.pdf text en public masters Universiti Putra Malaysia Big data Information retrieval MapReduce (Computer file)
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Big data Information retrieval MapReduce (Computer file)
spellingShingle	Big data Information retrieval MapReduce (Computer file) Ramadhan, Mazen Farid Ebrahim Indexing strategies of MapReduce for information retrieval in big data
description	In Information Retrieval (IR) the efficient strategy of indexing large dataset and terabyte-scale data is still an issue because of information overload as the result of increasing the knowledge, increasing the number of different media, increasing the number of platforms, and increasing the interoperability of platforms. Overall multiple processing machines MapReduce has been suggested as a suitable platform that use for distributing the intensive data operations. In this project, Sensei and Per-posting list indexing, Terrier will be analysed as they are the two most efficient MapReduce indexing strategies. The two indexing will be implemented in an existing framework of IR, and an experiment will be performed by using the Hadoop for MapReducing with the same large dataset, and try to analyse and verify the better efficient strategy between Sensei and Terrier. The experiment will measure the performance of retrieving when the size and processing power enlarge. The experiment examines how the indexing strategies scaled and work with large size of dataset and distributed number of different machines. The throughput will be measured by using MB/S (megabyte/per second), and the experiment results analyzing the performance of delay, consuming time and efficiency of indexing strategies between Sensei and Per-posting list indexing ,Terrier.
format	Thesis
qualification_level	Master's degree
author	Ramadhan, Mazen Farid Ebrahim
author_facet	Ramadhan, Mazen Farid Ebrahim
author_sort	Ramadhan, Mazen Farid Ebrahim
title	Indexing strategies of MapReduce for information retrieval in big data
title_short	Indexing strategies of MapReduce for information retrieval in big data
title_full	Indexing strategies of MapReduce for information retrieval in big data
title_fullStr	Indexing strategies of MapReduce for information retrieval in big data
title_full_unstemmed	Indexing strategies of MapReduce for information retrieval in big data
title_sort	indexing strategies of mapreduce for information retrieval in big data
granting_institution	Universiti Putra Malaysia
publishDate	2016
url	http://psasir.upm.edu.my/id/eprint/66723/1/FSKTM%202016%2025%20IR.pdf
_version_	1747812399115665408

Indexing strategies of MapReduce for information retrieval in big data

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