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EDBT
2010
ACM

Probabilistic threshold k nearest neighbor queries over moving objects in symbolic indoor space

8 years 6 months ago
Probabilistic threshold k nearest neighbor queries over moving objects in symbolic indoor space
The availability of indoor positioning renders it possible to deploy location-based services in indoor spaces. Many such services will benefit from the efficient support for k nearest neighbor (kNN) queries over large populations of indoor moving objects. However, existing kNN techniques fall short in indoor spaces because these differ from Euclidean and spatial network spaces and because of the limited capabilities of indoor positioning technologies. To contend with indoor settings, we propose the new concept of minimal indoor walking distance (MIWD) along with algorithms and data structures for distance computing and storage; and we differentiate the states of indoor moving objects based on a positioning device deployment graph, utilize these states in effective object indexing structures, and capture the uncertainty of object locations. On these foundations, we study the probabilistic threshold kNN (PTkNN) query. Given a query location q and a probability threshold T, this query ...
Bin Yang 0002, Hua Lu, Christian S. Jensen
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2010
Where EDBT
Authors Bin Yang 0002, Hua Lu, Christian S. Jensen
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