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.

I have finished my In-House Practical Training with the aim to improve my technical abilities in Android Application Development.
This In-House Practical training was to learn how to create an application in android, the Google operating system for mobile devices. The purpose of the final application is to help the student to have access to previous year papers and lab programs. It is a system of databases that allows the user to access previous year paper and lab programs. So I also used knowledge of databases. The design of such an application is made in slightly modified java.
This in-house practical training allowed me to increase my knowledge in java, a language with which I had many difficulties, but also to discover different aspects of android application development and wonderful people who made this In-House Practical Training really enriching for me.