EFFICIENT COMPUTATION OF RANGE AGGREGATES AGAINST UNCERTAIN LOCATION-BASED QUERIES

EFFICIENT COMPUTATION OF RANGE AGGREGATES AGAINST UNCERTAIN LOCATION-BASED QUERIES

In many applications, including location-based services, queries may not be precise. In this paper, we study the problem of efficiently computing range aggregates in a multidimensional space when the query location is uncertain. Specifically, for a query point Q whose location is uncertain and a set S of points in a multidimensional space, we want to calculate the aggregate (e.g., count, average and sum) over the subset S0 of S such that for each p 2 S0, Q has at least probability _ within the distance _ to p. We propose novel, efficient techniques to solve the problem following the filtering-and-verification paradigm. In particular, two novel filtering techniques are proposed to effectively and efficiently remove data points from verification. Our comprehensive experiments based on both real and synthetic data demonstrate the efficiency and scalability of our techniques.

Existing System:

The existing techniques for processing location-based spatial queries regarding certain query points and data points are not applicable or inefficient when uncertain queries are involved.
we investigate the problem of efficiently computing distance based range aggregates over certain data points and uncertain query points as described in the abstract. In general, an uncertain query Q is a multidimensional point that might appear at any location x following a probabilistic density function pdf within a region Q: region. There is a number of applications where a query point may be uncertain.

Proposed System:
We propose two novel filtering techniques, STF and APF, respectively. The STF technique has a decent filtering power and only requires the storage of very limited precomputed information. APF provides the flexibility to significantly enhance the filtering power by demanding more precomputed information to be stored. Both of them can be applied to continuous case and discrete case.
Extensive experiments are conducted to demonstrate the efficiency of our techniques.
. While we focus on the problem of range counting for uncertain location-based queries in the paper, our techniques can be immediately extended to other range aggregates.

Software Requirements:
.Net
Front End – ASP.Net
Language – C#.Net
Back End – SQL Server
Windows XP
Hardware Requirements:
RAM : 512 Mb
Hard Disk : 80 Gb
Processor : Pentium IV

FUTURE ENHANCEMENT:

We propose a general filtering and verification framework and two novel filtering techniques, named STF and APF, respectively, such that the expensive computation and IO cost for verification can be significantly reduced. Our experiments convincingly demonstrate the effectiveness and efficiency of our techniques.

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