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 booklet constitutes the refereed court cases of the tenth overseas convention on information Warehousing and information Discovery, DaWak 2008, held in Turin, Italy, in September 2008.

The forty revised complete papers awarded have been conscientiously reviewed and chosen from 143 submissions. The papers are prepared in topical sections on conceptual layout and modeling, olap and dice processing, dispensed info warehouse, facts privateness in facts warehouse, facts warehouse and knowledge mining, clustering, mining information streams, class, textual content mining and taxonomy, laptop studying strategies, and information 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

Example text

Herein, we focus on the autosale fact (represented as ) to analyse the automobile sales by means of the following measures ( ): the quantity sold, the price for sale, and their total amount. They are analysed around several dimensions ( ) that describe a sale: auto, time (tagged as a temporal dimension, {isTime}), dealership, salesperson, and customer. Each dimension contains several aggregation hierarchies to enable roll-up and drill-down OLAP operations [11] on sale data cubes. For instance, a customer can be viewed from different aggregation levels ( ): from customer data, analysts can aggregate ( ) automobile sales into cities, these into regions, and regions into states 2 .

Malinowski [20] proposes to model these hierarchies using bridge tables [1]. However, the traditional mapping must additionally include information about measure distribution and this information is keep on complex structures that had to be created by meaning a programming effort and a complexity added to the model. With our implementation we can compute a precise aggregation between complex hierarchies only using the spatial column that describe levels involved as is shown in the following query: Y Z X 30 O.

Concerning MDA, the proposed mappings relate two models with regards to the underneath platform: a “platformindependent model” (PIM) that represents a multidimensional model at the conceptual level, and two “platform-specific models” (PSMs) for representing both data-warehouse and OLAP metadata at the logical level with regards to a particular software technology but in a vendor-independent manner. For the PIM, we extend the “unified modelling language” (UML) [14] by means of our UML profile for multidimensional modelling of data warehouses [15].

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