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VICKY CHAWLA • Mar 5, 2016


Project Abstract / Summary : From the past two decades, the research area of nearest neighbor search in high dimensional data sets has always been in the limelight. Content-based multimedia indexing has been an active area of research as multimedia content is mapped into high-dimensional vectors of numbers, which are then stored in a high-dimensional index. For large collections, high-performance environments and large amount of main memory have been used. This paper reviews the NV-Tree (Nearest Vector Tree), a disk based data structure, which addresses the specific problem of locating the k-nearest neighbors within a collection of high dimensional data sets. The NV-tree is already used in industry to index more than 150 thousand hours of video for (very effective) near duplicate detection. We present a critical summary of published research literature pertinent to NV-Tree under contemplation for research. The purpose is to create familiarity with existing thinking and research on a particular topic, which may justify future research into a previously overlooked or understudied area.

Why did you choose to work on this project topic : Changing the existing modules or adding new ones can append improvements. Further enhancements can be made, a new approach to optimization using SSDs and its impact shall be proposed which can be used to improve the performance of NV-Trees. The Simulation model will be extended to address the properties of SSDs and study their impact on the NV-Tree implementation.

Project Highlights : Following are the reasons:-
1. For random data access in high-dimensional data SSD’s are used, which have no rotational delay and very short seek time.
2. Reduced maintenance cost.
3. Improved performance and a new approach for optimization.

So, i think this project is apt for the crazy engineers and its a topic to work and research on.

Project Category : CS / IT / Networking
Institute/College Name: AMITY UNIVERSITY
Participating Team From: Final Year

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