Data Warehousing and Knowledge Discovery

Data Warehousing and Knowledge Discovery

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Data Warehousing and Knowledge Discovery have been widely accepted as key te- nologies for enterprises and organizations to improve their abilities in data analysis, decision support, and the automatic extraction of knowledge from data. With the exponentially growing amount of information to be included in the decision-making process, the data to be processed become more and more complex in both structure and semantics. Consequently, the process of retrieval and knowledge discovery from this huge amount of heterogeneous complex data constitutes the reality check for research in the area. During the past few years, the International Conference on Data Warehousing and Knowledge Discovery (DaWaK) has become one of the most important international scientific events bringing together researchers, developers and practitioners. The DaWaK conferences served as a prominent forum for discussing latest research issues and experiences in developing and deploying data warehousing and knowledge d- covery systems, applications, and solutions. This yeara€™s conference, the Ninth Inter- tional Conference on Data Warehousing and Knowledge Discovery (DaWaK 2007), built on this tradition of facilitating the cross-disciplinary exchange of ideas, expe- ence and potential research directions. DaWaK 2007 sought to disseminate innovative principles, methods, algorithms and solutions to challenging problems faced in the development of data warehousing, knowledge discovery and data mining applications.We conducted our experiments on a Dell Inspiron 600m laptop with a Intel(R) Pentium(R) M 1.6GHz processor with 512 MB of RAM. ... dataset called 9Diamonds[11] consisting of 3, 000 objects with 9 natural clusters, Volcano[11] containing 1, 533 objects, Diabetes[12] containing ... Therefore, relying on human intuition, we employed a manual, interactive procedure that generated 80 pairs of parametersanbsp;...

Title:Data Warehousing and Knowledge Discovery
Author:Il Yeol Song, Johann Eder, Tho Manh Nguyen
Publisher:Springer Science & Business Media - 2007-08-21


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