Download Advances in Knowledge Discovery and Management: Volume 6 by Fabrice Guillet, Bruno Pinaud, Gilles Venturini PDF

By Fabrice Guillet, Bruno Pinaud, Gilles Venturini

This ebook offers a suite of consultant and novel paintings within the box of knowledge mining, wisdom discovery, clustering and type, in keeping with elevated and remodeled types of a range of the easiest papers initially awarded in French on the EGC 2014 and EGC 2015 meetings held in Rennes (France) in January 2014 and Luxembourg in January 2015. The booklet is in 3 elements: the 1st 4 chapters speak about optimization concerns in info mining. the second one half explores particular caliber measures, dissimilarities and ultrametrics. the ultimate chapters specialise in semantics, ontologies and social networks.
Written for PhD and MSc scholars, in addition to researchers operating within the box, it addresses either theoretical and useful points of information discovery and management.

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In 2012 IEEE 13th International Conference on Information Reuse and Integration (IRI) (pp. 316–323). , & Liang, D. (2003). Skyline with presorting. In Proceedings of ICDE 2003 (pp. 717–719). , & Liang, D. (2005). Skyline with presorting: Theory and optimizations. In Proceedings of IIS 2005 (pp. 595–604). Cole, M. (1991). Algorithmic skeletons: Structured management of parallel computation. Cambridge: MIT Press. Dubois, D. & Prade, H. (1984). On data summarization with fuzzy sets. In Proceedings of IFSA 1993 (pp.

These experimental results together confirm that the global computation time is θ (n 2 ∗ a) where a the number of attributes for each tuple. Some long duration results are somewhat imprecise, due to other processes in the system. 001 100 1000 10000 100000 Tuple number, 4 attributes Fig. 6 CUDA compared to sequential algorithm 1e+06 1e+07 36 H. Jaudoin et al. 400 100k tuples 200k tuples 500k tuples Computation Time (s) 350 300 250 200 150 100 50 0 0 5 10 15 20 25 30 35 40 Attribute number Fig. 7 Impact of the number of attributes These results show that it is possible to calculate a graded Skyline within a reasonable time, even on a large number of tuples with many attributes.

2001). Processing is done separately on subsets of data, then the results are merged. The second category relies on exploiting indices such as B-trees or R-trees to organize the data in order to avoid many dominance tests in Papadias et al. (2005), or like bitmap indices in Tan et al. (2001). The third category gathers various methods based 28 H. Jaudoin et al. on sorting: Block-Nested-Loops (BNL) (Börzsönyi et al. 2001) and its improvement named Sort-Filter-Skyline (Chomicki et al. 2003, 2005), and a strategy proposed in Bartolini et al.

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