Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Improved query performance with variant indexes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Range queries in OLAP data cubes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Selection of Views to Materialize in a Data Warehouse
ICDT '97 Proceedings of the 6th International Conference on Database Theory
Materialized Views Selection in a Multidimensional Database
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Algorithms for Materialized View Design in Data Warehousing Environment
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Data Warehouse Schema and Instance Design
ER '98 Proceedings of the 17th International Conference on Conceptual Modeling
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To select some "valuable" views for materialization is an essential challenge in OLAP system design. Several techniques proposed previously are not very scalable for systems with a large number of dimensional attributes in the very dynamic OLAP environment. In this paper, we propose two filtering methods. Our first method, the functional dependency filter, removes views with redundant summary information based on functional dependencies among the dimensional attributes. The second method, the size filter, is based on the view size to filter out any view that can be either derived from another small materialized view or has almost the same number of tuples as another materialized view from which it can be derived. More over, all useful views are selected by these two view filtering methods, other existing view selection methods can still be applied on the remaining views to further reduce other possible nonessential views from systems. We conduct performance tests to compare our method with other existing methods. The results show our method outperform the others.