Top-k selection queries over relational databases: Mapping strategies and performance evaluation
ACM Transactions on Database Systems (TODS)
Evaluating top-k queries over web-accessible databases
ACM Transactions on Database Systems (TODS)
Fast Approximate Similarity Search in Extremely High-Dimensional Data Sets
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
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Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Continuous monitoring of top-k queries over sliding windows
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Genetic algorithms for approximate similarity queries
Data & Knowledge Engineering
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IEEE Transactions on Knowledge and Data Engineering
A practical approach for efficiently answering top-k relational queries
Decision Support Systems
Region clustering based evaluation of multiple top-N selection queries
Data & Knowledge Engineering
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WISE '08 Proceedings of the 9th international conference on Web Information Systems Engineering
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Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
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Journal of Intelligent Information Systems
Adaptive relaxation for querying heterogeneous XML data sources
Information Systems
Efficient top-k search across heterogeneous XML data sources
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
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ACM Transactions on Database Systems (TODS)
MTopS: scalable processing of continuous top-k multi-query workloads
Proceedings of the 20th ACM international conference on Information and knowledge management
Approximating query answering on RDF databases
World Wide Web
Evaluating mid-(k, n) queries using b+-tree
DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications
Distributed top-k query processing by exploiting skyline summaries
Distributed and Parallel Databases
Range query estimation with data skewness for top-k retrieval
Decision Support Systems
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Top-k queries arise naturally in many database applications that require searching for records whose attribute values are close to those specified in a query. In this paper, we study the problem of processing a top-k query by translating it into an approximate range query that can be efficiently processed by traditional relational DBMSs. We propose a sampling-based approach, along with various query mapping strategies, to determine a range query that yields high recall with low access cost.Our experiments on real-world datasets show that, given the same memory budgets, our sampling-based estimator outperforms a previous histogram-based method in terms of access cost, while achieving the same level of recall. Furthermore, unlike the histogram-based approach, our sampling-based query mapping scheme scales well for high-dimensional data and is easy to implement with low maintenance cost.