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 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.

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