Coupling or decoupling for KNN search on road networks?: a hybrid framework on user query patterns
Proceedings of the 20th ACM international conference on Information and knowledge management
Top-k spatial keyword queries on road networks
Proceedings of the 15th International Conference on Extending Database Technology
An RFID and particle filter-based indoor spatial query evaluation system
Proceedings of the 16th International Conference on Extending Database Technology
Spatial-temporal query homogeneity for KNN object search on road networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Merged aggregate nearest neighbor query processing in road networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
G-tree: an efficient index for KNN search on road networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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In this paper, we present a new system framework called ROAD for spatial object search on road networks. ROAD is extensible to diverse object types and efficient for processing various location-dependent spatial queries (LDSQs), as it maintains objects separately from a given network and adopts an effective search space pruning technique. Based on our analysis on the two essential operations for LDSQ processing, namely, network traversal and object lookup, ROAD organizes a large road network as a hierarchy of interconnected regional subnetworks (called Rnets). Each Rnet is augmented with 1) shortcuts and 2) object abstracts to accelerate network traversals and provide quick object lookups, respectively. To manage those shortcuts and object abstracts, two cooperating indices, namely, Route Overlay and Association Directory are devised. In detail, we present 1) the Rnet hierarchy and several properties useful in constructing and maintaining the Rnet hierarchy, 2) the design and implementation of the ROAD framework, and 3) a suite of efficient search algorithms for single-source LDSQs and multisource LDSQs. We conduct a theoretical performance analysis and carry out a comprehensive empirical study to evaluate ROAD. The analysis and experiment results show the superiority of ROAD over the state-of-the-art approaches.