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 ...

• Type: Conference
• Year: 2010
• Where: EDBT
• Authors: Bin Yang 0002, Hua Lu, Christian S. Jensen
• Keywords: Database, EDBT 2010, Indoor Moving Objects, Indoor Positioning, Indoor Space