Abstract
The Similarity Join (SJ) has become one of the most popular and valuable data processing operators in analyzing large amounts of data. Various types of similarity join operators have been effectively used in multiple scenarios. However, these operators usually generate a large output size and many similar output pairs that represent almost the same information. In previous work, a new operator called Diversity Similarity Join (DSJ) has been proposed to address these issues. DSJ generates a smaller scale output and more meaningful and diverse result pairs. This operator, however, was proposed as a single node operator crucially limiting its scalability properties. In this paper, we propose the Distributed Diversity Similarity Join (D2SJ) operator, an approach that enables SJ diversification on big datasets. We present the design guidelines and implementation details on Apache Spark, a popular big data processing framework. Our experimental results with real-world high-dimensional data show that the proposed operator has excellent performance and scalability properties.
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Acknowledgments
This project was supported by an award from the Google Cloud Research program. The authors would like to thank Steven Hu, Timothy Raymer, and Steven Anderson for their contributions in the preliminary stages of this project.
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Silva, Y.N., Martinez, J., Castro Cea, P., Razente, H., Nardini Barioni, M.C. (2023). Diversity Similarity Join for Big Data. In: Pedreira, O., Estivill-Castro, V. (eds) Similarity Search and Applications. SISAP 2023. Lecture Notes in Computer Science, vol 14289. Springer, Cham. https://doi.org/10.1007/978-3-031-46994-7_20
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