SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Distance browsing in spatial databases
ACM Transactions on Database Systems (TODS)
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Time-parameterized queries in spatio-temporal databases
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Capturing the Uncertainty of Moving-Object Representations
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
K-Nearest Neighbor Search for Moving Query Point
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Nearest neighbor queries in road networks
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
Querying Imprecise Data in Moving Object Environments
IEEE Transactions on Knowledge and Data Engineering
On location models for ubiquitous computing
Personal and Ubiquitous Computing
An efficient and scalable approach to CNN queries in a road network
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Continuous nearest neighbor search
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Query processing in spatial network databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Voronoi-based K nearest neighbor search for spatial network databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
A Lattice-Based Semantic Location Model for Indoor Navigation
MDM '08 Proceedings of the The Ninth International Conference on Mobile Data Management
Efficient search for the top-k probable nearest neighbors in uncertain databases
Proceedings of the VLDB Endowment
Evaluating probability threshold k-nearest-neighbor queries over uncertain data
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Graph Model Based Indoor Tracking
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
Scalable continuous range monitoring of moving objects in symbolic indoor space
Proceedings of the 18th ACM conference on Information and knowledge management
Prox-RBAC: a proximity-based spatially aware RBAC
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
GMOBench: a benchmark for generic moving objects
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
A generic data model for moving objects
Geoinformatica
Daisy: the center for data-intensive systems at Aalborg University
ACM SIGMOD Record
An RFID and particle filter-based indoor spatial query evaluation system
Proceedings of the 16th International Conference on Extending Database Technology
Context-aware modelling of continuous location-dependent queries in indoor environments
Journal of Ambient Intelligence and Smart Environments - Context Awareness
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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 returns all subsets of k objects that have probability larger than T of containing the kNN query result of q. We propose a combination of three techniques for processing this query. The first uses the MIWD metric to prune objects that are too far away. The second uses fast probability estimates to prune unqualified objects and candidate result subsets. The third uses efficient probability evaluation for computing the final result on the remaining candidate subsets. An empirical study using both synthetic and real data shows that the techniques are efficient.