Complex Data Warehousing and Knowledge Discovery for by Tho Manh Nguyen

By Tho Manh Nguyen

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On the contrary, the second approach keeps track of the schema evolution, by using temporal validity labels. These labels are affixed either on dimension instances such as proposed by Bliujute, R. et al. A. , & Wrembel, R. (2004), Bebel, B. et al. (2004) and Body, M. et al. (2002). Let us detail these different methods. A. (2000) propose a temporal multidimensional model and a query language supporting it (namely TOLAP). Dimension elements are timestamped at the schema or instance level (or both) in order to keep track of the updates that occur.

Roussopoulos, Kotidis, & Roussopoulos, Cubetree: Organization of the bulk incremental updates on the data cube, 1997) in which the update set is first sorted and then merged with the current LBF R-tree in a single linear pass. Complete details are provided in (Eavis & Cueva, The LBF R-tree: Efficient Multidimensional Indexing with Graceful Degradation, 2007). As such, we are able to guaranteed persistent equivalency between the logical and physical ordering of data and index blocks. 5 coMPreSSIng the lBf r-tree Though the LBF R-tree represents an effective approach to improving data warehouse query performance, additional storage and performance benefits can be realized by extending the basic model so as to permit effective compression of the data and indexing mechanisms associated with both the primary fact tables and the largest aggregates.

To illustrate the concept, Figure 8(b) displays H -1 Hilbert two 2-d points along the same simple Rtmp curve. The initial point represents the reference ordinal stored in the block header, while the second point is the ordinal corresponding to the next tuple. In Figure 8(c) we see the Bit Compacted Differential form for the second point. Storage required is now just ρ = 9 - 6 = 310, or 112 in binary form. This is 62 bits less that the default encoding for a two dimensional value (assuming 32-bit integers).

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