The design and implementation of distributed INGRES
The INGRES papers: anatomy of a relational database system
Join processing in database systems with large main memories
ACM Transactions on Database Systems (TODS)
Optimization of dynamic query evaluation plans
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
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
Dynamic Query Operator Scheduling for Wide-Area Remote Access
Distributed and Parallel Databases
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
Scrambling query plans to cope with unexpected delays
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Ginga: a self-adaptive query processing system
Proceedings of the eleventh international conference on Information and knowledge management
Continual Queries for Internet Scale Event-Driven Information Delivery
IEEE Transactions on Knowledge and Data Engineering
Tradeoffs in Processing Complex Join Queries via Hashing in Multiprocessor Database Machines
VLDB '90 Proceedings of the 16th International Conference on Very Large Data Bases
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Dynamic Query Scheduling in Data Integration Systems
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
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Adaptive query processing in large distributed systems has seen increasing importance due to the rising environmental fluctuations in a growing Internet. We describe Ginga, an adaptive query processing engine that combines proactive (compile-time) alternative query plan generation with reactive (run-time) monitoring of network delays. The core of Ginga approach is the notion of adaptation space and mechanisms for coordinating and integrating different kinds of query adaptation. An adaptation space consists of a set of adaptation triggers and a set of adaptation cases associated with the triggers. Each adaptation case describes a specific adaptation opportunity of the query execution when changes to the runtime environment are detected. Our experimental results show that Ginga query adaptation can achieve significant performance improvements (up to 40% of response time gain) for processing distributed queries over the Internet.