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Efficient Computation of Range Aggregates against Uncertain Location Based Queries Abstract: 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 S_ of S such that for each p ∈ S_, Q has at least probability θ within the distance γ 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.. Algorithm Used: Filtering-and-Verification Algorithm: This motivates us to follow the filtering-and-verification paradigm for the uncertain aggregate query computation. Particularly, in the filtering phase, effective and efficient filtering techniques will be applied to prune or validate the points. The points that cannot be pruned or validated will be verified in the verification phase in which their falling probabilities are calculated. Therefore, it is desirable to develop effective and efficient filtering techniques to prune or validate points such that the number of points being verified can be significantly reduced. Input: RS : an aggregate R tree on data set S, Q : uncertain query, F : Filter, γ : query distance, θ : probabilistic threshold. Output: |Qθ,γ(S)| Description: 1: Queue := ∅ ; cn := 0; C := ∅ ; 2: Insert root of RS into Queue; 3: while Queue _= ∅ do 4: e ← dequeue from the Queue; 5: if e is validated by the filter F then 6: cn := cn + |e|; 7: else 8: if e is not pruned by the filter F then 9: if e is data entry then 10: C := C ∪ p where p is the data point e represented; 11: else 12: put all child entries of e into Queue; 13: end if 14: end if 15: end if 16: end while 17: for each point p ∈ C do 18: if Pfall(Q, p, γ) ≥ θ then 19: cn := cn + 1; 20: end if 21: end for 22: Return cn System Architecture: Existing System: Query imprecision or uncertainty may be often caused by the nature of many applications, including location based services. 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. Disadvantages: 1. Extensive experiments are conducted to demonstrate the efficiency of our techniques. 2. 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.. 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 pre-computed information. APF provides the flexibility to significantly enhance the filtering power by demanding more pre-computed information. 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. Advantages: 1. We will focus on evaluating the effectiveness of the proposed algorithms in capturing query relevance. 2. Relevance Measure 3. Online query grouping process 4. Similarity function Module Description: 1. Maximal and Minimal Distance Filtering(MMD) 2. Statistical Filtering(STF) 3. Probabilistically Constrained Regions (PCR) 4. Anchor Point Filtering(APF) Maximal and Minimal Distance Filtering (MMD): Since we can immediately apply Algorithm once the minimal and maximal distances between e and an anchor point are computed, it is less efficient to decide the accessing order after computing distances between all anchor points and e. Therefore we employ a simple heuristic to order anchor points without distance computations. We first consider the dimensional case, and the heuristic can be easily extended to higher dimensions. The maximal/minimal distance filtering technique is employed as a benchmark to evaluate the efficiency of other filtering techniques. Statistical Filtering (STF): We propose a statistical filtering technique, namely STF. After introducing the motivation of the technique, we present some important statistic information of the uncertain query and then show how to derive the lower and upper bounds of the falling probability of a point regarding an uncertain query Q, distance γ and probabilistic threshold θ. Probabilistically Constrained Regions (PCR): Although the statistical filtering technique can significantly reduce the candidate size in Algorithm, the filtering capacity is inherently limited because only a small amount of statistics are employed. This motivates us to develop more sophisticated filtering techniques to further improve the filtering capacity; that is, we aim to improve the filtering capacity with more pre-computations (i.e., more information kept for the filter). In this subsection, the PCR technique proposed in will be modified for this purpose. Anchor Point Filtering (APF): The anchor (pivot) point technique is widely employed in various applications, which aims to reduce the query computation cost based on some pre-computed anchor (pivot) points. In this subsection, we investigate how to apply anchor point technique to effectively and efficiently reduce the candidate set size. System Configuration:H/W System Configuration:Processor - Pentium –III Speed - 1.1 Ghz RAM - 256 MB(min) Hard Disk - 20 GB Floppy Drive - 1.44 MB Key Board - Standard Windows Keyboard Mouse - Two or Three Button Mouse Monitor - SVGA S/W System Configuration: Operating System Application Server Front End :Windows95/98/2000/XP : Tomcat5.0/6.X : Scripts HTML, Java, JSP,AJAX : JavaScript. Server side Script : Java Server Pages. Database Connectivity : Mysql.