Density-Based view materialization

Authors:
A. Das;D. K. Bhattacharyya
Affiliations:
Department of Information Technology, Tezpur University, Napaam, India;Department of Information Technology, Tezpur University, Napaam, India
Venue:
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Year:
2005

Citing 7
Cited 0

Implementing data cubes efficiently

SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
A progressive view materialization algorithm

Proceedings of the 2nd ACM international workshop on Data warehousing and OLAP
Materialized view selection under the maintenance time constraint

Data & Knowledge Engineering
Index Selection for OLAP

ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Materialized View Selection for Multidimensional Datasets

VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Storage Estimation for Multidimensional Aggregates in the Presence of Hierarchies

VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
An Efficient and Interactive A*-Algorithm with Pruning Power: Materialized View Selection Revisited

DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications

Quantified Score

Hi-index	0.01

Visualization

Abstract

View materialization or pre-computation of aggregates(views) is a well known technique used in data warehouse design and Decision Support System(DSS) to reduce the query response time. Obviously, all the views cannot be materialized due to space-time constraint. So, one important decision in designing Data Warehouse and DSS is to select the views to be materialized, which will reduce the query response time to the minimum limit in a DSS . This paper presents a density-based view materialization algorithm with average runtime complexity O(nlogn), where n is the number of views. We have used data cube lattice, view size, access frequency of the views and support(frequency) of the views in selecting the views to be materialized. Our algorithm works much faster and selects better views than other existing algorithms.