Spatio-temporal database research at the University of Melbourne
ACM SIGMOD Record
Aggregate farthest-neighbor queries over spatial data
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
Continuous visible nearest neighbor query processing in spatial databases
The VLDB Journal — The International Journal on Very Large Data Bases
On efficient obstructed reverse nearest neighbor query processing
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
See-to-retrieve: efficient processing of spatio-visual keyword queries
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
DART: an efficient method for direction-aware bichromatic reverse k nearest neighbor queries
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
Scalable visibility color map construction in spatial databases
Information Systems
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In many applications involving spatial objects, we are only interested in objects that are directly visible from query points. In this paper, we formulate the visible k nearest neighbor (VkNN) query and present incremental algorithms as a solution, with two variants differing in how to prune objects during the search process. One variant applies visibility pruning to only objects, whereas the other variant applies visibility pruning to index nodes as well. Our experimental results show that the latter outperforms the former. We further propose the aggregate VkNN query that finds the visible k nearest objects to a set of query points based on an aggregate distance function. We also propose two approaches to processing the aggregate VkNN query. One accesses the database via multiple VkNN queries, whereas the other issues an aggregate k nearest neighbor query to retrieve objects from the database and then re-rank the results based on the aggregate visible distance metric. With extensive experiments, we show that the latter approach consistently outperforms the former one.