Advances in Web-Age Information Management: Second by Jae-Woo Chang, Yeon-Jung Kim (auth.), X. Sean Wang, Ge Yu,

By Jae-Woo Chang, Yeon-Jung Kim (auth.), X. Sean Wang, Ge Yu, Hongjun Lu (eds.)

This ebook constitutes the refereed complaints of the second one overseas convention on Web-Age info administration, WAIM 2001, held in Xi'an, China, in July 2001.
The 21 revised complete papers and 12 brief papers awarded including four learn event papers have been conscientiously reviewed and chosen for inclusion within the lawsuits. The papers are geared up in topical sections on multimedia databases and high-dimensional indexing, details retrieval and textual content indexing, facts mining, semistructured info administration, info warehousing and federated databases, internet info administration and e-commerce, spatio-temporal and high-dimensional info administration, info mining and constraint administration, information integration and filtering, and workflow and adaptive systems.

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Additional info for Advances in Web-Age Information Management: Second International Conference, WAIM 2001 Xi’an, China, July 9–11, 2001 Proceedings

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Pestov, "On the geometry of similarity search: Dimensionality curse and concentration of measure", Technical Report RP-99-01, School of Mathematical and Computing Sciences, Victoria University of Wellington, New Zealand, January 1999. 7. Arya, S. , 'An Optimal Algorithm for Approximate Nearest Neighbor Searching’, In Proc. ACM-SIAM Symposium on Discrete Algorithms, pp. 573-582, 1994. 8. , 'Nearest Neighbor Queries', Proc. ACM SIGMOD Int. Conf. on Management of Data, 1995, pp. 71-79. 9. S. Berchtold, C.

85>. 5, 1) by definition 1. , S(CR11), is ‘01011011’ by the definition 2. In the same way, we can generate the cell signatures for the other MBRs. Fig. 3 depicts our high-dimensional index structure when we insert the eight feature vectors in Fig. 1. 2 New Distance Metric for Similarity Search For nearest neighbor search using the R*-tree, Roussopoulos[8] proposes an algorithm which reduces the number of visiting nodes by computing the minimum distance (MINDIST) and the minmax distance (MINMAXDIST) between a given query point and a MBR.

This paper proposes a method to utilize the correlation between query keywords in information retrieval toward extrapolating their semantic information.

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