Now-a-days most of the scientific and business applications require very large datasets for storage and manipulation and also high dimensionality is needed for achieving high accuracy. The high dimensionality and enormous size of such datasets pose very challenging problems in management, analysis, and retrieval of the datasets. The very large datasets crossing size even petabytes and high dimensionality its ranges vary from ten to several thousands. Most of the existing indexing structure is adequate to access vary large datasets and high dimensionality applications. This only motivate to design a new tool to access vary large datasets and high dimensionality effectively and efficiently. The main aim of this paper is to develop a new dynamic indexing structure to support vary 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 dimensio-nality 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 SRTree, 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 SRTree

Full text: IJEIR-1448_final.pdf