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As new data and updates are constantly arriving, the results of data miningapplications become stale and obsolete over time. Incremental processing is apromising approach to refreshing mining results. It utilizes previously savedstates to avoid the expense of re-computation from scratch.  In this paper, we propose i2MapReduce, a novel incremental processingextension to MapReduce, the most widely used framework for mining big data.Compared with the state-of-the-art work on Incoop, i2MapReduce (i) performskey-value pair level incremental processing rather than task levelre-computation, (ii) supports not only one-step computation but also moresophisticated iterative computation, which is widely used in data miningapplications, and (iii) incorporates a set of novel techniques to reduce I/Ooverhead for accessing preserved fine-grain computation states. We evaluatei2MapReduce using a one-step algorithm and three iterative algorithms withdiverse computation characteristics. Experimental results on Amazon EC2 showsignificant performance improvements of i2MapReduce compared to both plain anditerative MapReduce performing re-computation.

i2MapReduce: Incremental MapReduce for Mining Evolving Big Data