i2MapReduce: Incremental mapreduce for mining evolving big data

As new data and updates are constantly arriving, the results of data mining applications become stale and obsolete over time. Incremental processing is a promising approach to refresh mining results. It utilizes previously saved states to avoid the expense of re-computation from scratch. In this paper, we propose i
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MapReduce, a novel incremental processing extension to MapReduce. Compared with the state-of-the-art work on Incoop, i
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MapReduce (i) performs key-value pair level incremental processing rather than task level re-computation, (ii) supports not only one-step computation but also more sophisticated iterative computation, and (iii) incorporates a set of novel techniques to reduce I/O overhead for accessing preserved fine-grain computation states. Experimental results on Amazon EC2 show significant performance improvements of i
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MapReduce compared to both plain and iterative MapReduce performing re-computation.