Data Warehousing and Knowledge Discovery: 5th International by Peter Fankhauser, Thomas Klement (auth.), Yahiko Kambayashi,

By Peter Fankhauser, Thomas Klement (auth.), Yahiko Kambayashi, Mukesh Mohania, Wolfram Wöß (eds.)

This e-book constitutes the refereed lawsuits of the fifth foreign convention on information Warehousing and information Discovery, DaWaK 2003, held in Prague, Czech Republic in September 2003.

The forty-one revised complete papers provided have been rigorously reviewed and chosen from greater than a hundred thirty submissions. The papers are equipped in topical sections on facts cubes and queries, multidimensional facts types, net warehousing, swap detection, net mining and organization principles, organization ideas and selection timber, clustering, organization rule mining, information research and discovery, ontologies and bettering info caliber, queries and information styles, enhancing database question engines, and sampling and vector classification.

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Extra info for Data Warehousing and Knowledge Discovery: 5th International Conference, DaWak 2003, Prague, Czech Republic, September 3-5, 2003. Proceedings

Example text

This method is based on partitioning the category attributes of the source databases into ”common”, ”non-common”, and ”target” attributes. By summarizing on the non-common attributes first, we reduce the computational and space complexity of applying the linear indirect estimator method. We have shown that the PartialPre-aggregation method is, in general, significantly more efficient that the Fullcross-product method commonly used by statistical software. Furthermore, we provided a way to evaluate the optimal order of pairing databases for queries over more than two source summary databases.

But for the spatial dimension, there is little a priori knowledge available on how to define an appropriate hierarchy in advance. Spatial indexing has been one of the active focus areas in recent database research. There have been many data structures proposed for indexing spatial objects[6], such as Quadtree, R-tree (and its derivations such as R*-tree and R+-tree), and grid file. Among them, those indexes with tree structures provide nesting relationship between high-level nodes and low-level nodes.

Our aim is to find whether our approach is effective for spatial OLAP queries and more complex shapes such as line and polygon will be discussed in further work. Two data sets were generated according to the motivating example. The locations of gas stations for each data set comply with Gaussian distribution. By this, we attempted to reflect the fact that gas stations tend to be clustered around areas with dense road network. Table 1 gives the statistic information of these data sets: Table 1. The statistic data of base tables set # of gas stations # of customers # of transactions 1 2 1,000 10,000 200 800 8,663 34,685 Spatial index was built on each data set.

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