i2MapReduce: Incremental MapReduce for Mining Evolving Big Data

i2MapReduce: Incremental MapReduce for Mining Evolving Big Data

Abstract— 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 refreshing mining results. It utilizes previously saved states to avoid the expense of re-computation from scratch. In this paper, we propose I 2 MapReduce, a novel incremental processing extension to MapReduce, the most widely used framework for mining big data. Compared with the state-of-the-art work on Incoop, I 2 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, which is widely used in data mining applications, and (iii) incorporates a set of novel techniques to reduce I/O overhead for accessing preserved fine-grain computation states. We evaluate I 2 MapReduce using a one-step algorithm and four iterative algorithms with diverse computation characteristics. Experimental results on Amazon < Final Year Projects 2016 > EC2 show significant performance improvements of i2 MapReduce compared to both plain and iterative MapReduce performing re-computation.