A probabilistic filter protocol for Continuous Nearest-Neighbor Query

Zhu, Jianpeng; Jin, Jian; Wang, Ying

doi:10.1109/ycict.2009.5382333

Cited by 2 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The management of uncertain data has been studied extensively in the context of moving object databases [2,5,6,11,15,20,21,30,31]. Because location uncertainty is inherent in such databases, the development of efficient and effective solutions providing probabilistic query results is in high demand.…”

Section: Related Workmentioning

confidence: 99%

Moving range k nearest neighbor queries with quality guarantee over uncertain moving objects

Lee

Cho

Chung

et al. 2015

Information Sciences

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Moving range k nearest neighbor queries with quality guarantee over uncertain moving objects

Lee

Cho

Chung

et al. 2015

Information Sciences

View full text Add to dashboard Cite

“…Recently, the concept of filter bounds have been proposed, which controls when a sensor should report its value. In [9,10,11], the authors proposed filter bound protocols for nearest neighbor queries. The authors in [12,13,14] developed filter bound protocols for range and top-k queries.…”

Section: Related Workmentioning

confidence: 99%

“…[l(f i (ts)), u(f i (ts))) ; 6 Send filters to o i ; 7 tu ← ts; 8 Maintenance: 9 Upon receiving an update at timestamp t, trigger:…”

Section: Basic Filter Protocol (Bfp)mentioning

confidence: 99%

A filter-based protocol for continuous queries over imprecise location data

Jin

Cheng

Kao

et al. 2012

Proceedings of the 21st ACM International Conference on Information and Knowledge Management

View full text Add to dashboard Cite

In typical location-based services (LBS), moving objects (e.g., GPS-enabled mobile phones) report their locations through a wireless network. An LBS server can use the location information to answer various types of continuous queries. Due to hardware limitations, location data reported by the moving objects are often uncertain. In this paper, we study efficient methods for the execution of Continuous Possible Nearest Neighbor Query (CPoNNQ) that accesses imprecise location data. A CPoNNQ is a standing query (which is active during a period of time) such that, at any time point, all moving objects that have non-zero probabilities of being the nearest neighbor of a given query point are reported. To handle the continuous nature of a CPoNNQ, a simple solution is to require moving objects to continuously report their locations to the LBS server, which evaluates the query at every time step. To save communication bandwidth and mobile devices' batteries, we develop two filter-based protocols for CPoNNQ evaluation. Our protocols install "filter bounds" on moving objects, which suppress unnecessary location reporting and communication between the server and the moving objects. Through extensive experiments, we show that our protocols can effectively reduce communication costs while maintaining a high query quality.

show abstract

A probabilistic filter protocol for Continuous Nearest-Neighbor Query

Cited by 2 publications

References 8 publications

Moving range k nearest neighbor queries with quality guarantee over uncertain moving objects

Moving range k nearest neighbor queries with quality guarantee over uncertain moving objects

A filter-based protocol for continuous queries over imprecise location data

Contact Info

Product

Resources

About