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Incremental MapReduce for Data Mining

Publication Date : 31/12/2015

Author(s) :

Ms. Trupti M. Shinde. , Prof. S.V.Chobe.

Volume/Issue :
Volume 2
Issue 12
(12 - 2015)

Abstract :

As new data and updates are continuously arriving; the results of data mining applications turn out to be stale and obsolete over time. Incremental processing is a talented move towards to refreshing that mining results, it utilizes previously saved states to avoid the expense of re-computation from scratch. Incremental MapReduce, a work of fiction incremental processing extension to MapReduce, the mostly used structure for mining data. Compared with the state-of-the-art work on Incoop, Incremental MapReduce performs key-value pair level incremental processing somewhat than task level re-computation, supports not only one-step computation but also more sophisticated iterative computation, which is widely used in data mining applications, and incorporates a set of techniques to reduce I/O overhead for accessing preserved fine-grain computation states. Incremental MapReduce using a one-step algorithm and four iterative algorithms with diverse computation description works efficiently than existing Incoop.

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Incremental MapReduce for Data Mining

December 29, 2015