On conjunctive queries containing inequalities
Journal of the ACM (JACM)
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
On the complexity of the view-selection problem
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Efficient and extensible algorithms for multi query optimization
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Materialized view selection and maintenance using multi-query optimization
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Adapting materialized views after redefinitions: techniques and a performance study
Information Systems - Data warehousing
Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties
Access path selection in a relational database management system
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
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
Selection of Views to Materialize Under a Maintenance Cost Constraint
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Automated Selection of Materialized Views and Indexes in SQL Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Measuring the Complexity of Join Enumeration in Query Optimization
VLDB '90 Proceedings of the 16th 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
Automated database restructuring
Automated database restructuring
Index Selection for Databases: A Hardness Study and a Principled Heuristic Solution
IEEE Transactions on Knowledge and 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
Query evaluation using overlapping views: completeness and efficiency
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Physical design refinement: The ‘merge-reduce’ approach
ACM Transactions on Database Systems (TODS)
A framework for using materialized XPath views in XML query processing
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Exact and inexact methods for selecting views and indexes for OLAP performance improvement
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
Proceedings of the 17th ACM conference on Information and knowledge management
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We consider the problem of selecting views and indexes that minimize the evaluation costs of the important queries under an upper bound on the disk space available for storing the views/indexes selected to be materialized. We propose a novel end-to-end approach that focuses on systematic exploration of plans for evaluating the queries. Specifically, we propose a framework (architecture) and algorithms that enable selection of views/indexes that contribute to the most efficient plans for the input queries, subject to the space bound. We present strong optimality guarantees on our architecture. Our algorithms search for sets of competitive plans for queries expressed in the language of conjunctive queries with arithmetic comparisons. This language captures the full expressive power of SQL select-project-join queries, which are common in practical database systems. Our experimental results demonstrate the competitiveness and scalability of our approach.