The onion technique: indexing for linear optimization queries
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
Minimal probing: supporting expensive predicates for top-k queries
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Optimizing Multi-Feature Queries for Image Databases
VLDB '00 Proceedings of the 26th 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
Optimal aggregation algorithms for middleware
Journal of Computer and System Sciences - Special issu on PODS 2001
Towards Efficient Multi-Feature Queries in Heterogeneous Environments
ITCC '01 Proceedings of the International Conference on Information Technology: Coding and Computing
Algorithms and applications for answering ranked queries using ranked views
The VLDB Journal — The International Journal on Very Large Data Bases
Evaluating top-k queries over web-accessible databases
ACM Transactions on Database Systems (TODS)
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Making the Threshold Algorithm Access Cost Aware
IEEE Transactions on Knowledge and Data Engineering
Rank-Aware Query Processing and Optimization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Towards robust indexing for ranked queries
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Answering top-k queries using views
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
IO-Top-k: index-access optimized top-k query processing
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Optimizing top-k queries for middleware access: A unified cost-based approach
ACM Transactions on Database Systems (TODS)
Branch-and-bound processing of ranked queries
Information Systems
The Threshold Algorithm: From Middleware Systems to the Relational Engine
IEEE Transactions on Knowledge and Data Engineering
Efficient Skyline and Top-k Retrieval in Subspaces
IEEE Transactions on Knowledge and Data Engineering
Best position algorithms for top-k queries
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Ad-hoc aggregations of ranked lists in the presence of hierarchies
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A survey of top-k query processing techniques in relational database systems
ACM Computing Surveys (CSUR)
On Top-k Search with No Random Access Using Small Memory
ADBIS '08 Proceedings of the 12th East European conference on Advances in Databases and Information Systems
Dominant Graph: An Efficient Indexing Structure to Answer Top-K Queries
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Information Sciences: an International Journal
Efficient processing of exact top-k queries over disk-resident sorted lists
The VLDB Journal — The International Journal on Very Large Data Bases
Optimal top-k generation of attribute combinations based on ranked lists
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Subspace top-k query processing using the hybrid-layer index with a tight bound
Data & Knowledge Engineering
On the complexity of query result diversification
Proceedings of the VLDB Endowment
| Hi-index | 0.01 |
An important feature of the existing methods for ranked top-k processing is to avoid searching all the objects in the underlying dataset, and limiting the number of random accesses to the data. However, the performance of these methods degrades rapidly as the number of random accesses increases. In this paper, we propose a novel and general sequential access scheme for top-k query evaluation, which outperforms existing methods. We extend this scheme to efficiently answer top-k queries in subspace and on dynamic data. We also study the "dual" form of top-k queries called "ranking" queries, which returns the rank of a specified record/object, and propose an exact as well as two approximate solutions. An extensive empirical evaluation validates the robustness and efficiency of our techniques.