An improved OLAP join and aggregate algorithm based on dimension hierarchy

Authors:
Haitao He;Yanpeng Zhang;Jiadong Ren;Jiadong Ren;Changzhen Hu
Affiliations:
College of Information Science and Engineering, Yanshan University, Qinhuangdao, China;College of Information Science and Engineering, Yanshan University, Qinhuangdao, China;College of Information Science and Engineering, Yanshan University, Qinhuangdao, China;School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China;School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Year:
2009

Citing 9
Cited 0

An overview of data warehousing and OLAP technology

ACM SIGMOD Record
Improved query performance with variant indexes

SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Bitmap index design and evaluation

SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Query optimization for selections using bitmaps

SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Answering Queries with Aggregation Using Views

VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Constrained data clustering by depth control and progressive constraint relaxation

The VLDB Journal — The International Journal on Very Large Data Bases
Processing star queries on hierarchically-clustered fact tables

VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Multi-probe LSH: efficient indexing for high-dimensional similarity search

VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Transaction time indexing with version compression

Proceedings of the VLDB Endowment

Quantified Score

Hi-index	0.00

Visualization

Abstract

The OLAP (online analytical processing) queries are always involved with queries on the massive dataset. As a result, how to perform multi-table join and aggregate operations becomes the key issue. A Join and Aggregate Algorithm Based on Dimension Hierarchy (JABDH) is proposed in this paper. Considering the semantic characteristic which is not in all the dimension hierarchies, dimension hierarchical encoding is used to retrieve the matching dimension hierarchies and evaluate the set of query ranges for semantic dimension hierarchies. To improve the efficiency of multi-table join and aggregate operations for non-semantic dimensional hierarchies, join and aggregate operations are translated into bitmapped join index of fact table. The performance analysis and experimental results show that JABDH has improved the speed of queries and the efficiency of the OLAP queries.