Titlebook: Data Warehousing and Knowledge Discovery; 6th International Co Yahiko Kambayashi,Mukesh Mohania,Wolfram W?? Conference proceedings 2004 Spr

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David Zhang,Fangmei Chen,Yong Xue, lessons relate to the popularity of OLAP and data mining tools, the time spent on different tasks in BI projects, and the importance of “soft” people-related issues. Finally, the paper makes a recommendation on a number of future research topics that are useful in industry.

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Bringing Together Partitioning, Materialized Views and Indexes to Optimize Performance of Relationalan the materialized views and indexes. Based on rigorous experiments, we recommend the tuning parameters for better utilization of data partitioning, join indexes and materialized views to optimize the total cost.

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Novelty Framework for Knowledge Discovery in Databasesimportance that the user gives to different deviations. The computed degree of novelty is then compared with the user given threshold to report . rules to the user. We implement the proposed framework and experiment with some public datasets. The experimental results are quite promising.

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An Aggregate-Aware Retargeting Algorithm for Multiple Fact Data Warehousesions. The algorithm provides two types of transparency: a) aggregate unawareness, and b) MFTS join complexity unawareness. The algorithm requires simple metadata, and it is non-intrusive. The paper presents the retargeting algorithm, required metadata and performance experiments.

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GeoDWFrame: A Framework for Guiding the Design of Geographical Dimensional Schemastill some open questions, particularly regarding the design of a geographical dimensional data schema. This paper discusses some related work and proposes . that is a framework based on the star schema and has been specified as guidance for designing geographical dimensional schemas. Some experimental results are also given.

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Inductive Databases of Polynomial Equationsuse subsumption and evaluation constraints on polynomial equations. We evaluate this approach on standard regression problems. We finally consider IDBs containing patterns in the form of polynomial equations as well as molecular fragments, where the two are combined in order to derive QSAR (Quantitative Structure-Activity Relationships) models.

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