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Friday, 23 February 2018

Managing Multidimensional Historical Aggregate Data in Unstructured P2P Networks(2010)

Managing Multidimensional Historical Aggregate Data in Unstructured P2P Networks(2010)

Abstract
PEER-TO-PEER (P2P) networks have become very popular in the last few years. Nowadays, they are the most widespread approach for exchanging data among large communities of users in the file sharing context. Our aim is devising a P2P-based framework supporting the analysis of multidimensional historical data. Specifically, our efforts will be devoted to combining the amenities of P2P networks and data compression to provide a support for the evaluation of range queries, possibly trading off efficiency with accuracy of answers. The framework should enable members of an organization to cooperate by sharing their resources to host data and perform aggregate queries on them, while preserving their autonomy. For instance, consider the case of a worldwide virtual organization with users interested in geographical data, as well as the case of a real organization on an enterprise network. In both cases, even users who are not continuously interested in performing data analysis can make a part of their resources available for supporting analysis tasks needed by others,
Existing System
The problem of suitably extending-data-compression-based solutions to application contexts other than file sharing has not been deeply investigated yet. Specifically, no P2P-based solution has imposed itself as an effective evolution of traditional distributed databases. This is quite surprising, as the huge amount of resources
provided by P2P networks (in terms of storage capacity, computing power, and data transmission capability) could effectively support data management. From this standpoint, one of the application contexts which are likely to benefit from the support of a P2P network is the analysis of multidimensional data. Finally there is also a problem of storage space. Since we handle the multidimensional data there is no solution for space management.
Proposed System
The proposed solution for the above problem is given in 3 steps 1. First the partition of data and building an indexed aggregate structure over a multidimensional data population and store it in the server. 2. The indexed data is  distributed in a P2P network by the server and it will be stored in the location table 3. Finally the end user sends the query to the P2P network and access the data
Modules
  1. Detachment of the data
  2. Marking  
  3. Distributing the indexed data
  4. Data querying

1. Partitioning the data
The aim of the partitioning step is to divide the data domain into nonoverlapping blocks. For each of them, a portion of the amount of storage space B chosen to represent the whole synopsis will be invested. We denote the maximum amount of storage space which can be invested for summarizing a single block.
2. Marking  
In order to limit the space needed and also for the fast retrieving of data we create an index for each partitioned data. It will be stored in the server. Since data are historical, the storage space consumption can be reduced by adopting packing strategies for its construction, which aim at obtaining 100 percent space utilization.
3. Distributing the marked data
Once the indexing is over then the server can distribute the data in the P2P network. The data will be disturbed to the peers those who are available in the network. A location table is maintained in all the peers to store the data.
4. Data querying
End user or the client can send the query to the P2P network It will be search across the network and the data will be collected to compute the result and it will be send to the client .The searching is based on the name or the keyword that is stored on the location table.
Software Requirements:
  • Windows operating system 2000 and above
  • JDK 1.6
  • Tomcat Server
  • MySql
Hardware Requirements
Hard Disk: 20GB and Above
RAM: 512MB and Above
Processor: Pentium III and Above 

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