Eddies: continuously adaptive query processing
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Reducing the Braking Distance of an SQL Query Engine
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
LEO - DB2's LEarning Optimizer
Proceedings of the 27th International Conference on Very Large Data Bases
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
CORDS: automatic discovery of correlations and soft functional dependencies
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Optimizing Top-k Selection Queries over Multimedia Repositories
IEEE Transactions on Knowledge and Data Engineering
RankSQL: query algebra and optimization for relational top-k queries
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Consistently estimating the selectivity of conjuncts of predicates
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Supporting top-K join queries in relational databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Top-k query evaluation with probabilistic guarantees
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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Ranked retrieval plays an important role in explorative querying, where the user is interested in the top k results of complex ad-hoc queries. In such a scenario, response times are very important, but at the same time, tuning techniques, such as materialized views, are hard to use. Therefore it would be highly desirable to exploit the top-k property of the query to speed up the computation, reducing intermediate results and thus execution time. We present a novel approach to optimize ad-hoc top-k queries, propagating the top-k nature down the execution plan. Our experimental results support our claim that integrating top-k processing into algebraic optimization greatly reduces the query execution times and provides strong evidence that the resulting execution plans are robust against statistical misestimations.