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SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
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VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Continuous nearest neighbor search
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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Reverse kNN search in arbitrary dimensionality
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VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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Proceedings of the 2008 ACM SIGMOD international conference on Management of data
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The VLDB Journal — The International Journal on Very Large Data Bases
Indexing land surface for efficient kNN query
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Scalable shortest paths browsing on land surface
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Finding shortest path on land surface
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Monochromatic and bichromatic reverse nearest neighbor queries on land surfaces
Proceedings of the 21st ACM international conference on Information and knowledge management
Mobility increases the surface coverage of distributed sensor networks
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As geo-realistic rendering of land surfaces is becoming commonplace in geographical information systems (GIS), games and online Earth visualization platforms, a new type of k Nearest Neighbor (kNN) queries, "surface" k Nearest Neighbor (skNN) queries, has emerged and been investigated recently, which extends the traditional kNN queries to a constrained third dimension (i.e., land surface). All existing techniques, however, assume a static environment, limiting their utility in emerging applications (e.g., Location-based Services) where objects move. In this paper, for the first time, we propose two exact methods that can continuously answer skNN queries in a highly dynamic environment which allows for arbitrary movements of data objects. The first method, inspired by the existing techniques in monitoring kNN in road networks [7] maintains an analogous counterpart of the Dijkstra Expansion Tree on land surface, called Surface Expansion Tree (SE-Tree). However, we show the concept of expansion tree for land surface does not work as SE-tree suffers from intrinsic defects: it is fat and short, and hence does not improve the query efficiency. Therefore, we propose a superior approach that partitions SE-Tree into hierarchical chunks of pre-computed surface distances, called Angular Surface Index Tree (ASI-Tree). Unlike SE-tree, ASI-Tree is a well balanced thin and tall tree. With ASI-Tree, we can continuously monitor skNN queries efficiently with low CPU and I/O overheads by both speeding up the surface shortest path computations and localizing the searches. We experimentally verify the applicability and evaluate the efficiency of the proposed methods with both real world and synthetic data sets. ASI-Tree consistently and significantly outperforms SE-Tree in all cases.