Closest Keyword Search in Dynamic Multidimensional Data Sets

Abstract

Adding text to databases opens up many different innovations and functionalities that can be made feasible for keyword-based quests. The application in question focuses on search results that are keyword-marked and that are located in a geographical area. For these datasets, our main goal is to locate groups of points that satisfy search queries. Our team's recommendation is a process we call Projection and Multi Scale Hashing that combines random projection and hashing to provide great scalability and efficiency. This example illustrates how to present algorithms in both an exact and approximate manner. Analyses that take into account experimental and analytical studies show that, with regard to overall efficiency, multi-dimensional hashing offers up to 65 times better results. A point in a dynamic connection multi-dimensional feature space is a typical way to classify an object, and we often describe various objects as a point in a multi-dimensional feature space. In other words, for example, images are described using feature vectors that are comprised of colour components, and a textual description of the image is typically correlated with it (such as tags or keywords).