Combining fuzzy information from multiple systems (extended abstract)
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Computational geometry: algorithms and applications
Computational geometry: algorithms and applications
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
PREFER: a system for the efficient execution of multi-parametric ranked queries
SIGMOD '01 Proceedings of the 2001 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
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Query Processing Issues in Image(Multimedia) Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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
Branch-and-bound processing of ranked queries
Information Systems
Progressive and selective merge: computing top-k with ad-hoc ranking functions
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Dominant Graph: An Efficient Indexing Structure to Answer Top-K Queries
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Efficient processing of top-k join queries by attribute domain refinement
ADBIS'12 Proceedings of the 16th East European conference on Advances in Databases and Information Systems
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In this paper, we formulate a top-k query that compares objects in a database to a user-provided query object on a novel scoring function. The proposed scoring function combines the idea of attractive and repulsive dimensions into a general framework to overcome the weakness of traditional distance or similarity measures. We study the properties of the proposed class of scoring functions and develop efficient and scalable index structures that index the isolines of the function. We demonstrate various scenarios where the query finds application. Empirical evaluation demonstrates a performance gain of one to two orders of magnitude on querying time over existing state-of-the-art top-k techniques. Further, a qualitative analysis is performed on a real dataset to highlight the potential of the proposed query in discovering hidden data characteristics.