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

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

This ebook constitutes the refereed complaints of the second one foreign convention on destiny info and protection Engineering, FDSE 2015, held in Ho Chi Minh urban, Vietnam, in November 2015. The 20 revised complete papers and three brief papers offered have been conscientiously reviewed and chosen from 88 submissions. they've been prepared within the following topical sections: large facts analytics and big dataset mining; protection and privateness engineering; crowdsourcing and social community facts analytics; sensor databases and purposes in shrewdpermanent domestic and town; rising info administration structures and purposes; context-based research and functions; and information 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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5 [2], use the majority 1 Two classifiers are diverse if they make different errors on new datapoints [16]. Using Local Rules in Random Forests of Decision Trees A bootstrap sample of n individuals from the training set. Training set (n individuals, p attributes) Bootstrap-1 Bootstrap-2 DT-1 DT-2 ··· Bootstrap-t Split function: Gini index to perform a splitting using p’ atts randomly chosen from the p atts DT-t ··· x x x L1 (x) 35 x x x L2 (x) ··· Lt (x) Decision for a new individual x Classification: Majority-vote{DT1 (x), DT2 (x), .

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. In order to do that, they apply multiple prefix filtering technique, which is based on different global orderings, to their algorithm. Nevertheless, the algorithm behaves in a full-scan manner while our method performs a clustering technique which helps access the right data. Additionally, there is no mention of resolving the redundancy of string pairs as we do in our approach.

Moreover, it is totally redundant if we count duplicate signatures when computing similarity scores. In this paper, we use a set of shingles as the signatures of a document. We discard these duplicate shingles, therefore, from the very beginning of Map tasks, where data are at the first time read by mappers. Once the duplicates are removed, the list of shingles becomes the set of shingles, and the similarity problem turns out to be the overlap set problem [19]. Secondly, when obtaining candidate pairs, it would be useful to refine them in regard to the query object.

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