Issue No. 03 - March (2017 vol. 29)
Gang Chen , College of Computer Science, Zhejiang University, Hangzhou, China
Keyu Yang , College of Computer Science, Zhejiang University, Hangzhou, China
Lu Chen , College of Computer Science, Zhejiang University, Hangzhou, China
Yunjun Gao , College of Computer Science, Zhejiang University, Hangzhou, China
Baihua Zheng , School of Information Systems, Singapore Management University, Singapore
Chun Chen , College of Computer Science, Zhejiang University, Hangzhou, China
Given two object sets
Q and O, a metric similarity join finds similar object pairs according to a certain criterion. This operation has a wide variety of applications in data cleaning and data mining, to name but a few. However, the rapidly growing volume of data nowadays challenges traditional metric similarity join methods, and thus, a distributed method is required. In this paper, we adopt a popular distributed framework, namely, MapReduce, to support scalable metric similarity joins. To ensure the load balancing, we present two sampling based partition methods. One utilizes the pivot and the space-filling curve mappings to cluster the data into one-dimensional space, and then selects high quality centroids to enable equal-sized partitions. The other uses the KD-tree partitioning technique to equally divide the data after the pivot mapping. To avoid unnecessary object pair evaluation, we propose a framework that maps the two involved object sets in order, where the range-object filtering, the double-pivot filtering, the pivot filtering, and the plane sweeping techniques are utilized for pruning. Extensive experiments with both real and synthetic data sets demonstrate that our solutions outperform significantly existing state-of-the-art competitors.
Filtering, Extraterrestrial measurements, Load management, Algorithm design and analysis, Cleaning, Data mining
G. Chen, K. Yang, L. Chen, Y. Gao, B. Zheng and C. Chen, "Metric Similarity Joins Using MapReduce," in IEEE Transactions on Knowledge & Data Engineering, vol. 29, no. 3, pp. 656-669, 2017.