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Monday, August 17, 2015

QUERY PROCESSING IN P2P Network

QUERY PROCESSING IN P2P Network

          INTRODUCTION

1.1    OBJECTIVE :

• The main objective is to find out the approximate answers for the systems facing Dynamic Failure, in less time.

• We are going to do query processing in peer to peer network.

1.2    EXISTING SYSTEM :

          The existing system uses structured P2P network with Distributed Hash Table. In the existing system exact query processing is possible, but there are certain disadvantages.
§  Structured network is organized in such a way that data items are located at specific nodes in the network, and those nodes maintain some state information to enable efficient retrieval of data.
§  Structured nodes are not efficient and flexible enough for applications where nodes join or leave the network frequently.
§  The Sequential Algorithm is used which increases the latency of the project.
§  Since the nodes are selected sequentially, if any node gets disconnected , the exact answer is not received. And identification of the disconnected node becomes tedious and some times impossible.
  



1.3    PROPOSED SYSTEM :

The proposed system uses unstructured P2P network.
§  No assumptions about the location of the data items in the node are made in our project.
§  We can join the nodes at random times and depart without a prior notification.
§  We use approximate query processing to reduce the latency, which is the aim of our project.
§  It is possible to run the project by dynamically adding and removing the nodes.



































FEASIBILITY STUDY
2. FEASIBILITY STUDY

            2.1           SYSTEM ANALYSIS :

§  As P2P systems mature beyond file sharing applications and start getting deployed in increasingly sophisticated e-business and scientific environments, the vast amount of data within P2P databases poses a different challenge that has not been adequately researched.
§  Aggregation queries have the potential of finding applications in decision support, data analysis, and data mining. For example, millions of peers across the world may be cooperating on a grand experiment in astronomy, and astronomers may be interested in asking queries that require the aggregation of vast amounts of data covering thousands of peers.
  • There is real-world value for aggregation queries in Intrusion Detection Systems, and application signature analysis in P2P networks. 


            2.2  SYSTEM REQUIREMENTS :

            2.2.1  HARDWARE REQUIREMENTS :

·        Hard disk                     :   40 GB
·        RAM                            :  512 MB
·        Processor Speed          :  3.00GHz
·        Processor             :  Pentium  IV Processor

            2.2.2  SOFTWARE REQUIREMENTS :

·        Front End           : VS .NET 2005
·        Code Behind      : C#.net
·        Back End           : SQL SERVER 2000



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