i2MapReduce: Incremental MapReduce for Mining Evolving Big Data

TL;DR

i2MapReduce is an incremental extension to MapReduce that efficiently updates data mining results by reusing previous computations, supporting iterative algorithms, and reducing I/O overhead, leading to significant performance gains.

Contribution

The paper introduces i2MapReduce, a novel incremental processing framework that enhances MapReduce with fine-grain updates, iterative computation support, and I/O optimizations.

Findings

Significant performance improvements over standard MapReduce.

Effective support for iterative data mining algorithms.

Reduced I/O overhead in incremental processing.

Abstract

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 i2MapReduce, 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, i2MapReduce (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 i2MapReduce using a one-step algorithm and three iterative algorithms with diverse computation characteristics. Experimental results on Amazon EC2 show significant performance improvements of i2MapReduce compared to both plain and iterative MapReduce performing re-computation.