Research problems in data warehousing
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Rewriting aggregate queries using views
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Database tuning: principles, experiments, and troubleshooting techniques
Database tuning: principles, experiments, and troubleshooting techniques
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
A formal perspective on the view selection problem
The VLDB Journal — The International Journal on Very Large Data Bases
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Optimizing Queries with Materialized Views
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Materialized View Selection for Multidimensional Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Automated Selection of Materialized Views and Indexes in SQL Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Aggregate-Query Processing in Data Warehousing Environments
VLDB '95 Proceedings of the 21th 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
Answering Queries with Aggregation Using Views
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Answering queries using views: A survey
The VLDB Journal — The International Journal on Very Large Data Bases
Selecting and using views to compute aggregate queries
ICDT'05 Proceedings of the 10th international conference on Database Theory
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Data-intensive systems routinely use derived data (e.g., indexes or materialized views) to improve query-evaluation performance. We present a system architecture for Query-Performance Enhancement by Tuning (QPET), which combines design and use of derived data in an end-to-end approach to automated query-performance tuning. Our focus is on a tradeo. between (1) the amount of system resources spent on designing derived data and on keeping the data up to date, and (2) the degree of the resulting improvement in query performance. From the technical point of view, the novelty that we introduce is that we combine aggregate query rewriting techniques [1, 2] and view selection techniques [3] to achieve our goal.