Download Data Warehousing and Knowledge Discovery: 10th International by Matteo Golfarelli, Stefano Rizzi (auth.), Il-Yeol Song, PDF

By Matteo Golfarelli, Stefano Rizzi (auth.), Il-Yeol Song, Johann Eder, Tho Manh Nguyen (eds.)

This e-book constitutes the refereed complaints of the tenth overseas convention on information Warehousing and data Discovery, DaWak 2008, held in Turin, Italy, in September 2008.

The forty revised complete papers provided have been conscientiously reviewed and chosen from 143 submissions. The papers are equipped in topical sections on conceptual layout and modeling, olap and dice processing, disbursed facts warehouse, info privateness in facts warehouse, information warehouse and information mining, clustering, mining information streams, type, textual content mining and taxonomy, computing device studying recommendations, and knowledge mining applications.

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Extra info for Data Warehousing and Knowledge Discovery: 10th International Conference, DaWaK 2008 Turin, Italy, September 2-5, 2008 Proceedings

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The slice t is defined by the crossing of kt dimensions Dt = {d1 , . . , dr , . . , dkt } and the choice of a measure X. Let us note: - ni the size of the cell i ∈ t as the number of statistical units in the cell i, - nE the size of the sub-population E, we notice that ∀t, nE = i∈t ni , - Mdr the number of distinct values on the dimension dr (’ALL’ is excluded) (i) - xj the value of X for the statistical unit j in the cell i of the slice t. 6 Count Measure The problem we are interested in is to compute an indicator that expresses the level of interest for each expandable dimension according to a count measure.

We plan to extend this approach to consider advanced multidimensional properties such as degenerated facts and dimensions, also investigating how their logical OLAP counterparts are implemented. We would also investigate the derivation of specific OLAP metadata for non-traditional OLAP tools such as in mobile environments [17,18], as well as their enrichment with advanced 22 J. -N. Maz´ on, and J. Trujillo customisation issues such as OLAP preferences [19]. Apart from OLAP metadata, other analysis techniques, such as data mining [20], should be also considered.

Wiley Sons, Chichester (2002) 3. : Towards a spatial multidimensional model. In: DOLAP 2005: Proceedings of the 8th ACM international workshop on Data warehousing and OLAP, pp. 39–46. ACM, New York (2005) 4. : Representing spatiality in a conceptual multidimensional model. In: GIS 2004: Proceedings of the 12th annual ACM international workshop on Geographic information systems, pp. 12–22. ACM, New York (2004) 5. : Applying mda to the development of data warehouses. In: DOLAP 2005: Proceedings of the 8th ACM international workshop on Data warehousing and OLAP, pp.

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