22-05-2012, 04:19 PM
Efficient Skyline Computation In Structured
[attachment=22758]
ABSTRACT:
An increasing number of large-scale applications exploit peer-to-peer network architecture to provide highly scalable and flexible services. Among these applications, data management in peer-to-peer systems is one of the interesting domains. In this paper, we investigate the multidimensional skyline computation problem on a structured peer-to-peer network. In order to achieve low communication cost and quick response time, we utilize the iMinMax method to transform high-dimensional data to one-dimensional value and distribute the data in a structured peer-to-peer network called BATON. Thereafter, we propose a progressive algorithm with adaptive filter technique for efficient skyline computation in this environment. We further discuss some optimization techniques for the algorithm, and summarize the key principles of our algorithm into a query routing protocol with detailed analysis. Finally, we conduct an extensive experimental evaluation to demonstrate the efficiency of our approach.
EXISTING SYSTEM:
There are several approaches are attempted for a progressive processing of skyline queries on a CAN-based P2P network. The proposal controls query propagation based on the partial order of CAN’s zones. Unfortunately, it focuses on the constrained skyline queries and consequently, its load balancing approach has been designed to solve the workload imbalance caused by the skewed query ranges.
After that many researchers proposed the Skyline Space Partitioning (SSP) approach to compute skylines on a structured P2P network called BATON, which is a P2P network overlay based on a balanced binary tree structure. The advantage of the SSP is that it relaxes both network organization and skyline query propagation. However, the SSP has two disadvantages. First, at the early stage of each skyline query, the SSP misses a considerable number of peer nodes that have potential skyline points, which renders the result reporting less progressive. Second, during the query processing, the filtering points used by the SSP are not adaptive to the intermediate query results, which degenerates the filtering capacity.
PROPOSED SYSTEM:
In this project, we are going to simulate an efficient and progressive skyline computation approach in the structured P2P network BAlanced Tree Overlay Network (BATON). We use the BATON as our underlying P2P platform that naturally supports one dimensional index schemes. Thus, we can adopt a centralized index scheme to a distributed P2P network, so as to efficiently perform skyline queries and make the maintenance of the workload balance easier. The algorithm proposed in this paper exploits a data transformation mechanism, the BATON overlay, and a filtering-based candidate reduction strategy. Specifically, each d-dimensional data point is transformed into a one-dimensional value and then stored in BATON, which is organized as a balanced binary tree where each tree node corresponds to a real peer. A centralized B-tree-based skyline algorithm can then be easily adapted to such a setting. Apart from the progressiveness gained from the data transformation and distribution, the communication cost between peers is cut down by using an adaptive filtering technique. When a peer is processing a query, a portion of its local skyline points are selected as filtering points, which are used to safely prune peers without expected data points and remove unqualified intermediate answers on later involved peers.
No of nodes is infinite depends on the transmitter and receiver involved.
There is no dedicated line.
Fading is lesser than the existing system. When the fading is low then the performance of our system automatically increases.
[attachment=22758]
ABSTRACT:
An increasing number of large-scale applications exploit peer-to-peer network architecture to provide highly scalable and flexible services. Among these applications, data management in peer-to-peer systems is one of the interesting domains. In this paper, we investigate the multidimensional skyline computation problem on a structured peer-to-peer network. In order to achieve low communication cost and quick response time, we utilize the iMinMax method to transform high-dimensional data to one-dimensional value and distribute the data in a structured peer-to-peer network called BATON. Thereafter, we propose a progressive algorithm with adaptive filter technique for efficient skyline computation in this environment. We further discuss some optimization techniques for the algorithm, and summarize the key principles of our algorithm into a query routing protocol with detailed analysis. Finally, we conduct an extensive experimental evaluation to demonstrate the efficiency of our approach.
EXISTING SYSTEM:
There are several approaches are attempted for a progressive processing of skyline queries on a CAN-based P2P network. The proposal controls query propagation based on the partial order of CAN’s zones. Unfortunately, it focuses on the constrained skyline queries and consequently, its load balancing approach has been designed to solve the workload imbalance caused by the skewed query ranges.
After that many researchers proposed the Skyline Space Partitioning (SSP) approach to compute skylines on a structured P2P network called BATON, which is a P2P network overlay based on a balanced binary tree structure. The advantage of the SSP is that it relaxes both network organization and skyline query propagation. However, the SSP has two disadvantages. First, at the early stage of each skyline query, the SSP misses a considerable number of peer nodes that have potential skyline points, which renders the result reporting less progressive. Second, during the query processing, the filtering points used by the SSP are not adaptive to the intermediate query results, which degenerates the filtering capacity.
PROPOSED SYSTEM:
In this project, we are going to simulate an efficient and progressive skyline computation approach in the structured P2P network BAlanced Tree Overlay Network (BATON). We use the BATON as our underlying P2P platform that naturally supports one dimensional index schemes. Thus, we can adopt a centralized index scheme to a distributed P2P network, so as to efficiently perform skyline queries and make the maintenance of the workload balance easier. The algorithm proposed in this paper exploits a data transformation mechanism, the BATON overlay, and a filtering-based candidate reduction strategy. Specifically, each d-dimensional data point is transformed into a one-dimensional value and then stored in BATON, which is organized as a balanced binary tree where each tree node corresponds to a real peer. A centralized B-tree-based skyline algorithm can then be easily adapted to such a setting. Apart from the progressiveness gained from the data transformation and distribution, the communication cost between peers is cut down by using an adaptive filtering technique. When a peer is processing a query, a portion of its local skyline points are selected as filtering points, which are used to safely prune peers without expected data points and remove unqualified intermediate answers on later involved peers.
No of nodes is infinite depends on the transmitter and receiver involved.
There is no dedicated line.
Fading is lesser than the existing system. When the fading is low then the performance of our system automatically increases.