Fundamentals of algorithmics
Optimizing queries using materialized views: a practical, scalable solution
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Automated Selection of Materialized Views and Indexes in SQL Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
An Efficient Cost-Driven Index Selection Tool for Microsoft SQL Server
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
DB2 Advisor: An Optimizer Smart Enough to Recommend its own Indexes
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Automatic physical database tuning: a relaxation-based approach
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Recommending Materialized Views and Indexes with IBM DB2 Design Advisor
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
DB2 design advisor: integrated automatic physical database design
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Automatic SQL tuning in oracle 10g
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
To tune or not to tune?: a lightweight physical design alerter
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Physical Database Design: the database professional's guide to exploiting indexes, views, storage, and more
Physical design refinement: The ‘merge-reduce’ approach
ACM Transactions on Database Systems (TODS)
Self-tuning database systems: a decade of progress
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Robustness in automatic physical database design
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Constrained physical design tuning
Proceedings of the VLDB Endowment
Data mining-based materialized view and index selection in data warehouses
Journal of Intelligent Information Systems
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Physical database design tools rely on a DBA-provided workload to pick an “optimal” set of indexes and materialized views. Such an approach fails to capture scenarios where DBAs are unable to produce a succinct workload for an automated tool but still able to suggest an ideal physical design based on their broad knowledge of the database usage. Unfortunately, in many cases such an ideal design violates important constraints (e.g., space) and needs to be refined. In this paper, we focus on the important problem of physical design refinement, which addresses the above and other related scenarios. We propose to solve the physical refinement problem by using a transformational architecture that is based upon two novel primitive operations, called merging and reduction. These operators help refine a configuration, treating indexes and materialized views in a unified way, as well as succinctly explain the refinement process to DBAs.