The EXODUS optimizer generator
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
Grammar-like functional rules for representing query optimization alternatives
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
Optimization of parallel query execution plans in XPRS
Distributed and Parallel Databases - Selected papers from the first international conference on parallel and distributed information systems
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
On saying “Enough already!” in SQL
SIGMOD '97 Proceedings of the 1997 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
Cost-based query scrambling for initial delays
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Ripple joins for online aggregation
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Combining fuzzy information from multiple systems
Journal of Computer and System Sciences
Eddies: continuously adaptive query processing
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Scrambling query plans to cope with unexpected delays
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Exploiting statistics on query expressions for optimization
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Minimal probing: supporting expensive predicates for top-k queries
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Top-k selection queries over relational databases: Mapping strategies and performance evaluation
ACM Transactions on Database Systems (TODS)
Access path selection in a relational database management system
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
Reducing the Braking Distance of an SQL Query Engine
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Probabilistic Optimization of Top N Queries
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Optimizing Multi-Feature Queries for Image Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
LEO - DB2's LEarning Optimizer
Proceedings of the 27th International Conference on Very Large Data Bases
Supporting Incremental Join Queries on Ranked Inputs
Proceedings of the 27th International Conference on Very Large Data Bases
The Volcano Optimizer Generator: Extensibility and Efficient Search
Proceedings of the Ninth International Conference on Data Engineering
Query Processing Issues in Image(Multimedia) Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Towards Efficient Multi-Feature Queries in Heterogeneous Environments
ITCC '01 Proceedings of the International Conference on Information Technology: Coding and Computing
Evaluating Top-k Queries over Web-Accessible Databases
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Evaluating Refined Queries in Top-k Retrieval Systems
IEEE Transactions on Knowledge and Data Engineering
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Dynamic plan migration for continuous queries over data streams
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Robust query processing through progressive optimization
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Supporting top-k join queries in relational databases
The VLDB Journal — The International Journal on Very Large Data Bases
RankSQL: query algebra and optimization for relational top-k queries
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Discover: keyword search in relational databases
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Joining ranked inputs in practice
VLDB '02 Proceedings of the 28th 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
Lifting the burden of history from adaptive query processing
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Depth estimation for ranking query optimization
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Evaluating rank joins with optimal cost
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A survey of top-k query processing techniques in relational database systems
ACM Computing Surveys (CSUR)
Top-k Retrieval in Description Logic Programs Under Vagueness for the Semantic Web
SUM '07 Proceedings of the 1st international conference on Scalable Uncertainty Management
Ranking objects based on relationships and fixed associations
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Depth estimation for ranking query optimization
The VLDB Journal — The International Journal on Very Large Data Bases
Distributed top-k aggregation queries at large
Distributed and Parallel Databases
Location-aware privacy and more: a systems approach using context-aware database management systems
Proceedings of the 2nd SIGSPATIAL ACM GIS 2009 International Workshop on Security and Privacy in GIS and LBS
Optimal algorithms for evaluating rank joins in database systems
ACM Transactions on Database Systems (TODS)
Design and analysis of a ranking approach to private location-based services
ACM Transactions on Database Systems (TODS)
Run-time adaptivity for search computing
Search computing
Sharing work in keyword search over databases
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Chapter 11: rank-join algorithms for search computing
Search Computing
Distributed top-k query processing by exploiting skyline summaries
Distributed and Parallel Databases
A top-k query answering procedure for fuzzy logic programming
Fuzzy Sets and Systems
On saying "enough already!" in MapReduce
Proceedings of the 1st International Workshop on Cloud Intelligence
Top-k join queries: overcoming the curse of anti-correlation
Proceedings of the 17th International Database Engineering & Applications Symposium
Range query estimation with data skewness for top-k retrieval
Decision Support Systems
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Rank-aware query processing has emerged as a key requirement in modern applications. In these applications, efficient and adaptive evaluation of top-k queries is an integral part of the application semantics. In this article, we introduce a rank-aware query optimization framework that fully integrates rank-join operators into relational query engines. The framework is based on extending the System R dynamic programming algorithm in both enumeration and pruning. We define ranking as an interesting physical property that triggers the generation of rank-aware query plans. Unlike traditional join operators, optimizing for rank-join operators depends on estimating the input cardinality of these operators. We introduce a probabilistic model for estimating the input cardinality, and hence the cost of a rank-join operator. To our knowledge, this is the first effort in estimating the needed input size for optimal rank aggregation algorithms. Costing ranking plans is key to the full integration of rank-join operators in real-world query processing engines.Since optimal execution strategies picked by static query optimizers lose their optimality due to estimation errors and unexpected changes in the computing environment, we introduce several adaptive execution strategies for top-k queries that respond to these unexpected changes and costing errors. Our reactive reoptimization techniques change the execution plan at runtime to significantly enhance the performance of running queries. Since top-k query plans are usually pipelined and maintain a complex ranking state, altering the execution strategy of a running ranking query is an important and challenging task.We conduct an extensive experimental study to evaluate the performance of the proposed framework. The experimental results are twofold: (1) we show the effectiveness of our cost-based approach of integrating ranking plans in dynamic programming cost-based optimizers; and (2) we show a significant speedup (up to 300%) when using our adaptive execution of ranking plans over the state-of-the-art mid-query reoptimization strategies.