Exact Set Similarity Joins for Large Datasets in the GPGPU paradigm-Reference-Cited by-同舟云学术

Exact Set Similarity Joins for Large Datasets in the GPGPU paradigm

Published:2019 Issue: Volume: Page:
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Container-title:Proceedings of the 15th International Workshop on Data Management on New Hardware - DaMoN'19
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Author:

Bellas Christos¹,Gounaris Anastasios¹

Affiliation:

1. Aristotle University of Thessaloniki, Greece

Publisher

ACM Press

Reference27 articles.

1. Saman Ashkiani, Martin Farach-Colton, and John D Owens. 2018. A dynamic hash table for the GPU. In 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 419--429.

2. Ranieri Baraglia, Gianmarco De Francisci Morales, and Claudio Lucchese. 2010. Document Similarity Self-Join with MapReduce. In ICDM. 731--736.

3. Roberto J. Bayardo, Yiming Ma, and Ramakrishnan Srikant. 2007. Scaling up all pairs similarity search. In Proceedings of the 16th International Conference on World Wide Web, WWW 2007, Banff, Alberta, Canada, May 8-12, 2007. 131--140.

4. Christos Bellas and Anastasios Gounaris. 2017. GPU processing of theta-joins. Concurrency and Computation: Practice and Experience 29, 18 (2017).

5. Panagiotis Bouros, Shen Ge, and Nikos Mamoulis. 2012. Spatio-textual similarity joins. PVLDB 6, 1 (2012), 1--12.

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