ARES: a relational database with the capability of performing flexible interpretation of queries
IEEE Transactions on Software Engineering
VAGUE: a user interface to relational databases that permits vague queries
ACM Transactions on Information Systems (TOIS)
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Combining fuzzy information from multiple systems (extended abstract)
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
S3: similarity search in CAD database systems
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Two algorithms for nearest-neighbor search in high dimensions
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Supporting similarity queries in MARS
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
Efficient search for approximate nearest neighbor in high dimensional spaces
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Multidimensional access methods
ACM Computing Surveys (CSUR)
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
ACM Computing Surveys (CSUR)
Modern Information Retrieval
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Evaluating Top-k Selection 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
Fast Evaluation Techniques for Complex Similarity Queries
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
Efficient User-Adaptable Similarity Search in Large Multimedia Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
A General and Efficient Approach for Solving Nearest Neighbor Problem in the Vague Query System
WAIM '02 Proceedings of the Third International Conference on Advances in Web-Age Information Management
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
An Incremental Hypercube Approach for Finding Best Matches for Vague Queries
DEXA '99 Proceedings of the 10th International Conference on Database and Expert Systems Applications
The SH-tree: A Super Hybrid Index Structure for Multidimensional Data
DEXA '01 Proceedings of the 12th International Conference on Database and Expert Systems Applications
ISA - An Incremental Hyper-sphere Approach for Efficiently Solving Complex Vague Queries
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
VQS - A Vague Query System Prototype
DEXA '97 Proceedings of the 8th International Workshop on Database and Expert Systems Applications
Using Fagin's Algorithm for Merging Ranked Results in Multimedia Middleware
COOPIS '99 Proceedings of the Fourth IECIS International Conference on Cooperative Information Systems
An Efficient Searching Algorithm for Approximate Nearest Neighbor Queries in High Dimensions
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
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In this paper, we define a complex vague query as a multifeature nearest neighbor query. To answer such queries, the system must search on some feature spaces individually and then combine the results in order to find the final answers. The feature spaces are usually multidimensional and may consist of the sheer volume of data. Therefore searching costs are prohibitively expensive for complex vague queries. For only such a single-feature space, to alleviate the costs, problem of answering nearest neighbor and approximate nearest neighbor queries has been extensively studied and quite well addressed in the literature. This paper, however, introduces an approach for finding (1+驴)- approximate nearest neighbors of complex vague queries, which must deal with the problem on multiple feature spaces. This approach is based on a novel, efficient and general algorithm called ISA-Incremental hyper-Sphere Approach [12, 13], which has recently been introduced for solving nearest neighbor problem in the VQS-Vague Query System [22]. To the best of our knowledge, the work presented in this paper is one of the vanguard solutions for generally dealing with problem of approximate multi-feature nearest neighbor queries. The experimental results will prove the efficiency of the proposed approach.