ACM Transactions on Database Systems (TODS)
Exploiting inter-operation parallelism in XPRS
SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
Query evaluation techniques for large databases
ACM Computing Surveys (CSUR)
Multiple query optimization with Depth-First Branch-and-Bound and dynamic query ordering
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Improvements on a heuristic algorithm for multiple-query optimization
Data & Knowledge Engineering
Workload scheduling for multiple query processing
Information Processing Letters
Scheduling problems in parallel query optimization
PODS '95 Proceedings of the fourteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Simultaneous optimization and evaluation of multiple dimensional queries
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Cost-based optimization of decision support queries using transient-views
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient and extensible algorithms for multi query optimization
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Introduction to Algorithms
Access path selection in a relational database management system
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
Common expression analysis in database applications
SIGMOD '82 Proceedings of the 1982 ACM SIGMOD international conference on Management of data
On the Multiple-Query Optimization Problem
IEEE Transactions on Knowledge and Data Engineering
Using Common Subexpressions to Optimize Multiple Queries
Proceedings of the Fourth International Conference on Data Engineering
Materialized View Selection for Multidimensional Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Anatomy of a Mudular Multiple Query Optimizer
VLDB '88 Proceedings of the 14th International Conference on Very Large Data Bases
The Volcano Optimizer Generator: Extensibility and Efficient Search
Proceedings of the Ninth International Conference on Data Engineering
Redbrick Vista: Aggregate Computation and Management
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Multiple Query Optimization by Cache-Aware Middleware Using Query Teamwork
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
On tuning and optimization for multiple queries in databases
On tuning and optimization for multiple queries in databases
Multiple query optimization in middleware using query teamwork
Software—Practice & Experience
Optimizing complex queries with multiple relation instances
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Exact cardinality query optimization for optimizer testing
Proceedings of the VLDB Endowment
Energy-efficient query management scheme for a wireless sensor database system
EURASIP Journal on Wireless Communications and Networking - Special issue on theoretical and algorithmic foundations of wireless ad hoc and sensor networks
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Database systems frequently have to execute a set of related queries, which share several common subexpressions. Multi-query optimization exploits this, by finding evaluation plans that share common results. Current approaches to multi-query optimization assume that common subexpressions are materialized. Significant performance benefits can be had if common subexpressions are pipelined to their uses, without being materialized. However, plans with pipelining may not always be realizable with limited buffer space, as we show. We present a general model for schedules with pipelining, and present a necessary and sufficient condition for determining validity of a schedule under our model. We show that finding a valid schedule with minimum cost is NP-hard. We present a greedy heuristic for finding good schedules. Finally, we present a performance study that shows the benefit of our algorithms on batches of queries from the TPCD benchmark.