Para-Join: an efficient parallel method for string similarity join Online publication date: Tue, 12-Sep-2017
by Cairong Yan; Jian Wang; Bin Zhu; Wenjing Guo
International Journal of High Performance Computing and Networking (IJHPCN), Vol. 10, No. 4/5, 2017
Abstract: In big data area, a significant challenge about string similarity join is to find all similar pairs more efficiently. In this paper, we propose an efficient parallel method, called Para-Join which first splits the input into small sets according to the joint-frequency vector and the interval-vector of each string, and then joins the pairs for each small set in parallel. Para-RR algorithm and Para-RS algorithm are proposed to extend partion-based algorithm and adopt the multi-threading technique to implement the string similarity join within each set and between two different sets separately. We prove that Para-Join method can not only avoid reduplicate computation but also ensure the completeness of the result. We also put forward an effective pruning strategy to improve the performance. Experimental results show that our method achieves high efficiency and significantly outperforms state-of-the-art approaches.
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