A Rapid Dimension Hierarchical Aggregation Algorithm on High Dimensional Olap

Author

Hu, Kong-fa ; Chen, Ling ; Liu, Hai-dong ; Liu, Jia-jia ; Zhang, Chang-hai

Author_Institution

Dept. of Comput. Sci. & Eng., Yangzhou Univ.

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

1547

Lastpage

1551

Abstract

In the high dimensional DW, we full materialized the data cube impossibly. In this paper, we propose a novel aggregation algorithm, DHEPA, to vertically partition a high dimensional dataset into a set of disjoint low dimensional datasets called fragment mini-cubes. Using inverted hierarchical encoding indices and pre-aggregated results, OLAP queries are computed online by dynamically constructing cuboids from the fragment mini-cubes. As a result, the method we proposed in this paper can greatly reduce the disk I/Os and highly improve the efficiency of OLAP queries

Keywords

data mining; data warehouses; encoding; query processing; DHEPA; data warehouses; fragment mini-cubes; high dimensional OLAP queries; high dimensional dataset; inverted hierarchical encoding indices; low dimensional dataset; online analysis process; rapid dimension hierarchical aggregation algorithm; Algorithm design and analysis; Clustering algorithms; Computer science; Cybernetics; Data engineering; Data warehouses; Databases; Encoding; Machine learning; Machine learning algorithms; Material storage; Partitioning algorithms; Snow; On-line analysis process (OLAP); dimension hierarchical encoding; hierarchical aggregation algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258826

Filename

4028310