94.inconstant database system

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1. Head office: 3nd floor, Krishna Reddy Buildings, OPP: ICICI ATM, Ramalingapuram, Nellore www.pvrtechnology.com, E-Mail: pvrieeeprojects@gmail.com, Ph: 81432 71457…
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  • 1. Head office: 3nd floor, Krishna Reddy Buildings, OPP: ICICI ATM, Ramalingapuram, Nellore www.pvrtechnology.com, E-Mail: pvrieeeprojects@gmail.com, Ph: 81432 71457 Abstract The importance of query processing over uncertain data has recently arisen due to its wide usage in many real-world applications. In the context of uncertain databases, previous works have studied many query types such as nearest neighbor query, range query, top-k query, skyline query, and similarity join. In this paper, we focus on another important query, namely, probabilistic group nearest neighbor (PGNN) query, in the uncertain database, which also has many applications. a PGNN query retrieves data objects that minimize the aggregate distance. Due to the inherent uncertainty of data objects, previous techniques to answer group nearest neighbor (GNN) query cannot be directly applied to our PGNN problem. Motivated by this, we propose effective pruning methods, namely, spatial pruning and probabilistic pruning, to reduce the PGNN search space, which can be seamlessly integrated into our PGNN query procedure. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approach. Existing System Many techniques for answering queries (e.g., nearest neighbor (NN) query and range query) assume that data objects are precise, they cannot be directly applied to handle uncertain data (otherwise, inaccuracy or even errors may be introduced). Thus, it is crucial to design novel approaches to efficiently and accurately answer queries over uncertain objects. In the context of uncertain databases,previous works have studied query types such as range query NN query , top-k query, skyline query , and similarity join .  Existing systems uses query types such as nearest neighbor query, range query, top-k query, skyline query, and similarity join.  Multiple query method , Single point method and Minimum bounded method is used for data retrieval. Disadvantages
  • 2. Head office: 3nd floor, Krishna Reddy Buildings, OPP: ICICI ATM, Ramalingapuram, Nellore www.pvrtechnology.com, E-Mail: pvrieeeprojects@gmail.com, Ph: 81432 71457  Effective answering only for precise query points.  Searching process is done in a very large search space.  No knowledge about the size of the database.  Pruning algorithms are not implemented Proposed System In an uncertain database,however,each data object has “dynamic” attributes, which means that the value of an attribute locates within a range with some probability. Therefore,the pairwise distance between any two objects is no longer a constant; instead, it is a variable. Correspondingly, we have to redefine the GNN query over uncertain objects. In particular, in an uncertain database,a probabilistic group nearest neighbor (PGNN) query retrieves a set of uncertain objects such that their probability of being GNN is greater than a user-specified probability threshold . Proposed system uses probability based nearest neighbor query. Pruning algorithms are used to limit the search space such as - spatial pruning - Probabilistic pruning Advantages  Effective answering for precise and uncertain query points.  Searching process is limited with pruning algorithms.
  • 3. Head office: 3nd floor, Krishna Reddy Buildings, OPP: ICICI ATM, Ramalingapuram, Nellore www.pvrtechnology.com, E-Mail: pvrieeeprojects@gmail.com, Ph: 81432 71457  Detailed knowledge about the size of the database. MODULES: The modules that are included in this project are 1.Group Nearest Neighbor (GNN) Query 2.Spatial Pruning 3.Probabilistic Pruning Module 1: Group Nearest Neighbor (GNN) Query This Module retrieves data objects in the database that minimize their sum aggregate distances .GNN query with aggregate distance function is also called aggregate nearest neighbor (ANN) query. Three methods to answer GNN queries. The first proposed approach, namely Multiple Query Method (MQM) applies to retrieve the data point that minimizes the score. The second approach, namely, the Single Point Method (SPM) it computes the lower bound of the sum aggregate distance from object. The third approach, namely, Minimum Bounding Method (MBM) uses a minimum bounding rectangle (MBR)a minimum bounding rectangle is considered as the representative of query points and can be applied to facilitate pruning data points.
  • 4. Head office: 3nd floor, Krishna Reddy Buildings, OPP: ICICI ATM, Ramalingapuram, Nellore www.pvrtechnology.com, E-Mail: pvrieeeprojects@gmail.com, Ph: 81432 71457 Retrievesdata objects Aggregate Distances setof query points Uncertain Data Base Retrieve datapoint that minimizes score Computesthe lowerbound of sum aggregate distance Queryprocedure to facilitate pruningdatapoints Data objectsare indexedbya multidimensional structure
  • 5. Head office: 3nd floor, Krishna Reddy Buildings, OPP: ICICI ATM, Ramalingapuram, Nellore www.pvrtechnology.com, E-Mail: pvrieeeprojects@gmail.com, Ph: 81432 71457 Module 2: Spatial Pruning The spatial pruning module uses to prune those uncertain objects in the database. In other words to filter out those objects with Zero expected probability.This method reduces the PGNN search space by pruning those data objects. The spatial pruning method discards those data objects with the expected probability equal to zero. Prune Uncertain Objects Filter those Objects with Zero Expected Probability Reduce the PGNN Search Space Data base
  • 6. Head office: 3nd floor, Krishna Reddy Buildings, OPP: ICICI ATM, Ramalingapuram, Nellore www.pvrtechnology.com, E-Mail: pvrieeeprojects@gmail.com, Ph: 81432 71457 Module 3:Probabilistic Pruning Probabilistic pruning approach, which utilizes the precomputed information from object distributions in their uncertainty regions. This method only utilizes the geometric property of uncertain objects (i.e., uncertainty regions). The probabilistic pruning aims to prune those objects with probability never greater than where is a probability threshold specified by PGNN queries. This method can prune data objects Using upper bounds & lower bounds .
  • 7. Head office: 3nd floor, Krishna Reddy Buildings, OPP: ICICI ATM, Ramalingapuram, Nellore www.pvrtechnology.com, E-Mail: pvrieeeprojects@gmail.com, Ph: 81432 71457 Software and Hardware Requirements:  HardwareInterface: Hard disk :40GB RAM :512MB Processor : Pentium IV  Prune Data objects using upper bounds & lower bounds Reduce the PGNN Search Space Data base Indexin g Refinemen t
  • 8. Head office: 3nd floor, Krishna Reddy Buildings, OPP: ICICI ATM, Ramalingapuram, Nellore www.pvrtechnology.com, E-Mail: pvrieeeprojects@gmail.com, Ph: 81432 71457  SoftwareInterface: JDK 1.5  Tomcat  Servlets,jsp  SQL Server
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