Efficient Retrieval from Multi-dimensional Dataset Based on Nearest Keyword

TLDR: Hash-based index structure is implemented along with a new algorithm called as ProMiSH, i.e., Projection and Multi-scale Hashing, to achieve higher scalability with the increasing data and speedup of retrieval of results for the users.

Abstract: Search engine deals with the user provided query to deliver the informative results to users. The volume of data associated with these search engines is very vast and it becomes very difficult to handle just data. These data are dynamic, increase day by day, and hence many techniques have been proposed to handle such dynamic data. Existing Tree-based techniques are mostly applicable to queries such as spatial queries, range queries, and many more. These techniques are useful only for the queries that have coordinates. But these techniques are not applicable to the queries which do not have coordinates. Keyword-based search has been considered more helpful in many applications and tools. The paper considers objects, i.e., images, tagged with number of keywords and those will be embedded into vector space for evaluation. The main aim here is to achieve higher scalability with the increasing data and speedup of retrieval of results for the users. A query is been implemented in the paper called as nearest keyword set query that deals with multi-dimensional datasets. Hash-based index structure is implemented along with a new algorithm called as ProMiSH, i.e., Projection and Multi-scale Hashing.