By Vicky Nassis, R. Rajugan, Tharam S. Dillon, Wenny Rahayu (auth.), Yahiko Kambayashi, Mukesh Mohania, Wolfram Wöß (eds.)
This e-book constitutes the refereed lawsuits of the sixth overseas convention on information Warehousing and information Discovery, DaWaK 2004, held in Zaragoza, Spain, in September 2004.
The forty revised complete papers provided have been conscientiously reviewed and chosen from over a hundred submissions. The papers are geared up in topical sections on info warehouse layout; wisdom discovery framework and XML facts mining, info cubes and queries; multidimensional schema and knowledge aggregation; inductive databases and temporal principles; business purposes; info clustering; facts visualization and exploration; info category, extraction, and interpretation; facts semantics, organization rule mining; occasion series mining; and trend mining.
Read Online or Download Data Warehousing and Knowledge Discovery: 6th International Conference, DaWaK 2004, Zaragoza, Spain, September 1-3, 2004. Proceedings PDF
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Example text
Mohania, and B. Bhargava, “Partjoin : an efficient storage and query execution design strategy for data warehousing”, Proc. Int. Conf. on Data Warehousing and Knowledge Discovery (DAWAK), pp. 296-306, 2002. 6. L. Bellatreche, M. Schneider, Lorinquer, H. and M. php 7. S. Chaudhuri and V. , “An efficient cost-driven index selection tool for microsoft sql server”, in Proc. Int. Conf. on Very Large Databases (VLDB), 1997, pp. 146-155. Bringing Together Partitioning, Materialized Views and Indexes 25 8.
Figure 2 shows an example of a GDW based on this framework. In the Figure 2, Temperature, Precipitation and Time are non-geometrical dimensions, Region-name is a fully-geometrical dimension, area and count are numerical measures and region-map is a spatial measure. GeoDWFrame 29 Fig. 2. GDW model of Han et. al [10] With regard to the research of Han et al, there are four issues that need to be further discussed. These issues motivated the proposal of the GeoDWFrame. The first debates the use of spatial measures.
For each query, we report the extra space used either by the partitioning or by the indexes, the query time, the update time for UD and UW, and for two values of and 10), the values of and for two values of (5 and 25). The results are reported in the three tables Table 1, Table 2, Table 3 (one for each series). 3 Comments Based on these results, the following comments are issued: Query Time: We observe that partitioning gives a profit even for a low selectivity. The profit is very important with a high selectivity (when the number of different values for 20 Ladjel Bellatreche et al.