Name
Abstract | ||
|---|---|---|
MOLAP is a important application on multidimensional data warehouse. We often execute range queries on aggregate cube computed by pre-aggregate technique in MOLAP. For the cube with d dimensions, it can generate 2d cuboids. But in a high-dimensional cube, it might not be practical to build all these cuboids. In this paper, we propose a multi-dimensional hierarchical fragmentation of the fact table based on multiple dimension attributes and their dimension hierarchical encoding. This method partition the high dimensional data cube into shell mini-cubes. The proposed data allocation and processing model also supports parallel I/O and parallel processing as well as load balancing for disks and processors. We have compared the methods of shell mini-cubes with the other existed ones such as partial cube and full cube by experiment. The results show that the algorithms of mini-cubes proposed in this paper are more efficient than the other existed ones. |
Year | DOI | Venue |
|---|---|---|
2005 | null | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
multi-dimensional hierarchical fragmentation,proposed data allocation,partial cube,full cube,parallel shell mini-cubes,dimension hierarchical encoding,shell mini-cubes,multidimensional data warehouse,aggregate cube,high dimensional data cube,high-dimensional cube,computing high dimensional molap,parallel processing,range query,process model,high dimensional data,load balance | Fact table,Computer science,Range query (data structures),Parallel computing,Cuboid,MOLAP,Online analytical processing,Klee–Minty cube,Data cube,Cube | Conference |
Volume | Issue | ISSN |
3613 LNAI | null | 16113349 |
ISBN | Citations | PageRank |
3-540-28312-9 | 1 | 0.41 |
References | Authors | |
6 | 5 | |