Comparing MapReduce-Based k-NN Similarity Joins on Hadoop for High-Dimensional Data

Similarity joins represent a useful operator for data mining, data analysis and data exploration applications. With the exponential growth of data to be analyzed, distributed approaches like MapReduce are required. So far, the state-of-the-art similarity join approaches based on MapReduce mainly focused on the processing of low-dimensional vector data. In this paper, we revisit and investigate the performance of different MapReduce-based approximate k-NN similarity join approaches on Apache Hadoop for large volumes of high-dimensional vector data.