Storing and Indexing Spatial Data in P2P Systems Essay

STORING AND INDEXING SPATIAL DATA IN P2P SYSTEMS ABSTRACT: The peer-to-peer (P2P) paradigm has become very popular for storing and sharing information in a totally decentralized manner. At first, research focused on P2P systems that host 1D data. Nowadays, the need for P2P applications with multidimensional data has emerged, motivating research on P2P systems that manage such data. The majority of the proposed techniques are based either on the distribution of centralized indexes or on the reduction of multidimensional data to one dimension.

Our goal is to create from scratch a technique that is inherently distributed and also maintains the multidimensionality of data. Our focus is on structured P2P systems that share spatial information. We present SPATIALP2P, a totally decentralized indexing and searching framework that is suitable for spatial data. SPATIALP2P supports P2P applications in which spatial information of various sizes can be dynamically inserted or deleted, and peers can join or leave. The proposed technique preserves well locality and directionality of space.

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EXISTING SYSTEM: THE peer-to-peer (P2P) paradigm has become very popular for storing and sharing information in a totally decentralized manner. Typically, a P2P system is a distributed environment formed by autonomous peers that operate in an independent manner. Each peer stores a part of the available information and maintains links (indexes) to other peers. P2P systems Until recently, research has focused mostly on P2P systems that handle 1D data such as strings and numbers.

However, the need for P2P applications that manage multidimensional data has emerged. These systems pose additional requirements that stem from the particularities of such data. In centralized multidimensional applications, information is stored according to its multidimensional extent using an indexing structure (e. g. , R-tree). Typically, these structures preserve the locality and the directionality of multidimensional information. Intuitively, locality implies that neighboring multidimensional information is stored in neighboring nodes, while irectionality implies that the index structure preserves orientation. The notions of locality and directionality are very important. If an index structure preserves these properties then searching in the index corresponds to Searching in the multidimensional space which can highly improve query evaluation cost There are exist two different paradigms for handling multidimensional data in P2P systems. 1) The first paradigm proposes the use of a distributed version of some centralized multidimensional index.

The main challenge of these approaches is to avoid the bottleneck occurring at the peer storing the root of the tree 2) The second paradigm maps the multidimensional data into a single dimension and uses a Distributed Hash Table (DHT) [3], [7], [15], [22]. Briefly, 1D DHT techniques use a distance metric to define the locality of the 1D data. Then, peers use this distance to store data and index other peers The re usage of techniques in the approaches in both categories leads to the maintenance of some fundamental features that oppose to the nature of either the distributedness or the multidimensionality.

PROPOSED SYSTEM: Research in P2P systems has recently expanded in the domain of multidimensional data. The techniques that have been proposed until now belong to two broad categories. Techniques in the first category are based on the idea that 1D index’s can be reused in order to manage multidimensional data, if the dimensionality is reduced to one. This idea was the first to be explored. Techniques in the second category are based on the idea that centralized hierarchical indexes can be reused to manage dispersed multidimensional data, if they are properly distributed.

More elaborated solutions have been proposed in this category. However, the reusage of existing techniques in the approaches in both categories leads to the maintenance of some fundamental features that oppose to the nature of either the distributedness or the multidimensionality. Our intention is to overcome these shortcomings by creating a technique that manages disperse multidimensional data in an inherently distributed way without altering the dimensionality. In this work, we have focused on spatial data.

We have presented the SPATIALP2P framework for handling spatial data in a P2P network. SPATIALP2P provides efficient storing, indexing and searching services by preserving locality and directionality. As a result, SPATIALP2P performs exceptionally well for point and range query operations. SPATIALP2P supports dynamic insertion and deletion of spatial information of various sizes and dynamic joining and leaving of peers. In the future, we intend to adjust and test the SPATIALP2P framework for data of higher dimensions.

SYSTEM REQUIREMENT HARDWARE REQUIREMENTS Processor : Any Processor above 500 MHz. Ram : 512Mb. Hard Disk : 40Gb. Compact Disk : 650 Mb. Input device : Standard Keyboard and Mouse. Output device : VGA and High Resolution Monitor. SOFTWARE REQUIREMENTS Operating System : Windows XP or above Language : C# Language Data Bases : SQL Server 2005 Front End : ASP. Net

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