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Probabilistic Continuous Update Scheme in Location Dependent Continuous Queries

Authors: Song Han 0002; Edward Chan;

Probabilistic Continuous Update Scheme in Location Dependent Continuous Queries

Abstract

It is difficult to maintain the exact location of mobile objects due to the limited resources in a mobile network. A consequence of this problem is that the update cost for a location-dependent continuous query for moving objects can be quite high using traditional methods. In this paper, we propose a probabilistic update method to maintain the fidelity of the query results without incurring significant update cost. Our scheme makes use of two types of updates, one to keep the uncertainty of the mobile object’s position within a specific confidence interval, and the other using probability that the moving object’s location uncertainty will affect the query result as the threshold to decide whether an update should be generated or not. The effectiveness of our approach is demonstrated using a series of simulation experiments.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Average
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