Chi Zhang
Graduated with PhD Fall 2013. Now at Walmart Labs.

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  • Efficient Parallel kNN Joins for Large Data in MapReduce (Project Website), Talk
    By Chi Zhang,    Feifei Li,    Jeffrey Jestes
    In Proceedings of 15th International Conference on Extending Database Technology (EDBT 2012),  pages 38-49,  March,  2012.

    In data mining applications and spatial and multimedia databases, a useful tool is the kNN join, which is to produce the k nearest neighbors (NN), from a dataset S, of every point in a dataset R. Since it involves both the join and the NN search, performing kNN joins efficiently is a challenging task. Meanwhile, applications continue to witness a quick (exponential in some cases) increase in the amount of data to be processed. A popular model nowadays for large-scale data processing is the shared-nothing cluster on a number of commodity machines using MapReduce. Hence, how to execute kNN joins efficiently on large data that are stored in a MapReduce cluster is an intriguing problem that meets many practical needs. This work proposes novel (exact and approximate) algorithms in MapReduce to perform efficient parallel kNN joins on large data. We demonstrate our ideas using Hadoop. Extensive experiments in large real and synthetic datasets, with tens or hundreds of millions of records in both R and S and up to 30 dimensions, have demonstrated the efficiency, effectiveness, and scalability of our methods.