Encapsulation of parallelism in the Volcano query processing system
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Join processing in relational databases
ACM Computing Surveys (CSUR)
Query evaluation techniques for large databases
ACM Computing Surveys (CSUR)
Dataflow query execution in a parallel main-memory environment
Distributed and Parallel Databases - Selected papers from the first international conference on parallel and distributed information systems
Efficient mid-query re-optimization of sub-optimal query execution plans
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Cost-based query scrambling for initial delays
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
An adaptive query execution system for data integration
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Eddies: continuously adaptive query processing
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Database System Implementation
Database System Implementation
Robust query processing through progressive optimization
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Adapting to changing resource performance in grid query processing
DMG 2005 Proceedings of the First VLDB conference on Data Management in Grids
Time-completeness trade-offs in record linkage using adaptive query processing
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Adaptive join processing in pipelined plans
Proceedings of the 13th International Conference on Extending Database Technology
Automation everywhere: autonomics and data management
BNCOD'07 Proceedings of the 24th British national conference on Databases
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Adaptive query processors make decisions as to the most effective evaluation strategy for a query based on feedback received while the query is being evaluated. In essence, any of the decisions made by the optimizer (e.g., on operator order or on which operators to use) may be revisited in an adaptive query processor. This paper focuses on changes to physical operators (e.g., the specific join operators used, such as hash-join or merge-join) in pipelined query evaluators. In so doing, the paper characterizes the runtime properties of pipelined operators in a way that makes explicit when specific operators may be replaced, and that allows the validity of operator replacements to be proved. This is illustrated with reference to the substitution of join operators during their evaluation.