Distributed Multi Scale Hashing on Efficient Nearest Keyword Set Search for peer-to-peer Multidimensional Dataset Model

Abstract

The growing rise of peer-to-peer (P2P) multidimensional dataset models has boosted demand for effective nearest keyword set search approaches. In this paper, we suggest a novel approach termed Distributed Multi-Scale Hashing (DMSH) to overcome the issues associated with keyword-based search in P2P systems. DMSH combines the advantages of multi-scale hashing and distributed computing to accomplish scalable and accurate nearest keyword set retrieval. DMSH's distributed architecture efficiently distributes the computational burden across numerous nodes, ensuring efficient and parallel processing. The multi-scale hashing algorithm offers effective indexing and retrieval of multidimensional datasets, reducing search space and enhancing search efficiency. We illustrate the effectiveness and scalability of DMSH in comparison to existing techniques using extensive experiments on real-world datasets. The study contributes to the area by offering a distributed method for effective nearest keyword set search in P2P multidimensional dataset models. The suggested DMSH algorithm has higher performance, allowing users to efficiently extract relevant data from large-scale dispersed datasets