By Matteo Golfarelli, Stefano Rizzi (auth.), Il-Yeol Song, Johann Eder, Tho Manh Nguyen (eds.)
This publication constitutes the refereed court cases of the tenth foreign convention on information Warehousing and information Discovery, DaWak 2008, held in Turin, Italy, in September 2008.
The forty revised complete papers awarded have been rigorously reviewed and chosen from 143 submissions. The papers are geared up in topical sections on conceptual layout and modeling, olap and dice processing, allotted facts warehouse, information privateness in info warehouse, info warehouse and knowledge mining, clustering, mining info streams, class, textual content mining and taxonomy, computing device studying recommendations, and knowledge mining applications.
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Extra resources for Data Warehousing and Knowledge Discovery: 10th International Conference, DaWaK 2008 Turin, Italy, September 2-5, 2008 Proceedings
Net/ 11. org/mda/ 12. htm 13. : Object-based selective materialization for eﬃcient implementation of spatial data cubes. IEEE Transactions on Knowledge and Data Engineering 12(6), 938–958 (2000) 14. : Toward better support for spatial decision making: Deﬁning the characteristics of spatial on-line analytical processing. Geomatica 55(4), 539–555 (2001) 15. : A UML proﬁle for multidimensional modeling in data warehouses. Data Knowl. Eng. 59(3), 725–769 (2006) 16. org 17. org 18. formal/03-03-02 19.
In this paper, we only detail the method developped in the case of a count measure. The restitution is done by a color scale applied on each dimension which proposes to the end-user the dimensions rank. 2 Working Example We illustrate our approach with an example based on a database issued from a marketing context. In this example, we consider a simpliﬁed detailed table with the following relation scheme: Customer(#Cust, Tariff, Dwelling, Heating, Occupation, Water Heating, Oldness Of House, Date Of Contract Subscription, Type Of Dwelling, Consumption).
Overview of our MDA approach for MD modeling of SDW repository composed of measures or fact attributes (FactAttribute, FA), with respect to dimensions, each aggregation level of a hierarchy is speciﬁed by classes stereotyped as Base ( B ). Every Base class can contain several dimension attributes (DimensionAttribute, DA ) and must also contain a Descriptor attribute (Descriptor, D). An association stereotyped as Rolls-upTo (Rolls-upTo, ) between Base classes speciﬁes the relationship between two levels of a classiﬁcation hierarchy.