By Matteo Golfarelli, Stefano Rizzi (auth.), Il-Yeol Song, Johann Eder, Tho Manh Nguyen (eds.)
This publication constitutes the refereed lawsuits 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 offered 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 information warehouse, facts warehouse and knowledge mining, clustering, mining information streams, category, textual content mining and taxonomy, desktop studying recommendations, and information mining applications.
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For the PIM, we extend the “unified modelling language” (UML) [14] by means of our UML profile for multidimensional modelling of data warehouses [15]. On the other hand, our PSM are represented in CWM [14] as we stated in the previous discussion: the repository structures with the Relational package [12] and the analysis structures with the OLAP package. , independently of any tool, thus focusing only on the problems involved in the conceptual–logical mapping. For implementing this one, we use the “query/view/transformation” (QVT) [14] language that contains a declarative part to easily design model-to-model relations.
G. g. ,bridge tables [1]) to support spatial data decreasing the simplicity and intuitiveness of the model. Even more, to the best of our knowledge, there is no effort developed for aligning the design of SDWs with the general MDA paradigm. In order to overcome these pitfalls, we developed the present approach for the development of SDWs. 4 A MDA Implementation for Spatial Data Warehouses Development In this section, the MD modeling of a SDW is aligned with MDA. We show how to define (i) the main MDA artifacts for spatial MD modeling, and (ii) a set of QVT transformation rules between these models.
While exploring the cube, end-users are rapidly confronted by analyzing a huge number of drill paths according to the different dimensions. Generally, analysts are only interested in a small part of them which corresponds to either high statistical associations between dimensions or atypical cell values. This paper fits in the scope of discovery-driven dynamic exploration. It presents a method coupling OLAP technologies and mining techniques to facilitate the whole process of exploration of the data cube by identifying the most relevant dimensions to expand.