The design and analysis of spatial data structures
The design and analysis of spatial data structures
A vector space model for automatic indexing
Communications of the ACM
Modern Information Retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Discovering Colocation Patterns from Spatial Data Sets: A General Approach
IEEE Transactions on Knowledge and Data Engineering
Hybrid index structures for location-based web search
Proceedings of the 14th ACM international conference on Information and knowledge management
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Keyword Search on Spatial Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Efficient retrieval of the top-k most relevant spatial web objects
Proceedings of the VLDB Endowment
Discovering spatial interaction patterns
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
Detecting nearly duplicated records in location datasets
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Database research at the National University of Singapore
ACM SIGMOD Record
Analyzing the composition of cities using spatial clustering
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
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Advances in web technology have given rise to new information retrieval applications. In this paper, we present a model for geographical region search and call this class of query similar region query. Given a spatial map and a query region, a similar region search aims to find the top-k most similar regions to the query region on the spatial map. We design a quadtree based algorithm to access the spatial map at different resolution levels. The proposed search technique utilizes a filter-and-refine manner to prune regions that are not likely to be part of the top-k results, and refine the remaining regions. Experimental study based on a real world dataset verifies the effectiveness of the proposed region similarity measure and the efficiency of the algorithm.