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The main aim of this paper is to develop a new dynamic indexing structure to support very large datasets and high dimensionality. This new structure is tree based used to facilitate efficient access. It is highly adaptable to any type of applications. The newly developed structure is based on nearest neighbors’ method with exception of linearly scan the very large datasets. The NewTree surely minimizes adverse effect of the curse of dimensionality. It means that the most existing indexing techniques degrade rapidly when dimensionality goes higher. The major drawback here is the retrieval of subsets from the huge storage system. The NewTree structure can handle very efficiently and effectively during adding new data. When the new data are added and the shape of the structure does not change. The performance of the newly developed structure can be evaluated with SR Tree, existing indexing structure. The results clearly show that the efficiency of the newly developed structure is superior in both time complexity and memory complexity than SR Tree.

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