Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
An array-based algorithm for simultaneous multidimensional aggregates
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
View selection using randomized search
Data & Knowledge Engineering
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Fast Computation of Sparse Datacubes
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Materialized View Selection for Multidimensional Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
On the Computation of Multidimensional Aggregates
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
QC-trees: an efficient summary structure for semantic OLAP
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Top-Down Computation of Partial ROLAP Data Cubes
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 8 - Volume 8
CURE for cubes: cubing using a ROLAP engine
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
High Performance Analytics with the R3-Cache
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
| Hi-index | 0.00 |
Over the past ten to fifteen years, data warehouse platformshave grown enormously, both in terms of their importance and their sheer size. Traditionally, such systems have been based upon a dimensional model known as the Star Schema that consists of a central fact table and a series of related dimension tables. Given the enormous size of the fact table, virtually all current systems augment the primary fact table with a small number of focused summary tables. Previous research has addressed the issue of the selection or identification of the most cost-effective summaries. However, the problem of efficiently computing a given view subset has received far less attention. In this paper, we present a suite of greedy algorithms for the construction of these view subsets. Experimental results demonstrate cost savings of between 20% and 70% relative to the naive alternatives, depending upon the degree of materialization required.