Future Data and Security Engineering: Second International by Tran Khanh Dang, Roland Wagner, Josef Küng, Nam Thoai,

By Tran Khanh Dang, Roland Wagner, Josef Küng, Nam Thoai, Makoto Takizawa, Erich Neuhold

This booklet constitutes the refereed complaints of the second one overseas convention on destiny facts and safety Engineering, FDSE 2015, held in Ho Chi Minh urban, Vietnam, in November 2015. The 20 revised complete papers and three brief papers provided have been conscientiously reviewed and chosen from 88 submissions. they've been prepared within the following topical sections: giant information analytics and large dataset mining; protection and privateness engineering; crowdsourcing and social community information analytics; sensor databases and purposes in clever domestic and town; rising facts administration structures and purposes; context-based research and purposes; and knowledge versions and advances in question processing.

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Extra resources for Future Data and Security Engineering: Second International Conference, FDSE 2015, Ho Chi Minh City, Vietnam, November 23-25, 2015, Proceedings

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Examples on the Fly Our proposed methods are packaged into two MapReduce phases. The first phase is to ahead of time prepare the data whilst the second one is to on-demand process the queries. Each phase consists of Map and Reduce tasks. In order to get insight of the proposed methods, we introduce a step-by-step example in a nutshell. Assuming that there are two different data sources where the set of documents {Doc1, Doc2, Doc3} belongs to the first one whereas the set of documents {Doc4, Doc5, Doc6} belongs to the other.

6] present a three-phase MapReduce-based algorithm for string similarity joins in that the first MapReduce operation is for the filter stage and the last two MapReduce operations are for the verification stage. In the verification stage, their algorithm needs, however, to re-access the original datasets whilst our approach only accesses the datasets once from the beginning stage. Rong et al. [19] also introduce a three-phase MapReduce algorithm for string similarity join. Their objective is to reduce the number of candidate string pairs as well.

155–162 (2009) 13. : V-SMART-join: a scalable MapReduce framework for all-pair similarity joins of multisets and vectors. PVLDB 5(8), 704–715 (2012) 14. : Distributed indexing for semantic search. In: Proceedings of the 3rd International Semantic Search Workshop, pp. 1–4 (2010) 15. : An efficient similarity search in large data collections with MapReduce. , Thoai, N. ) FDSE 2014. LNCS, vol. 8860, pp. 44–57. Springer, Heidelberg (2014) 16. : An elastic approximate similarity search in very large datasets with MapReduce.

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