Big Data Retrieval Using Locality-Sensitive Hashing with Document-Based NoSQL Database

Abstract

A locality-sensitive hashing (LSH) method in the document-based NoSQL database is proposed for enhancing the ability of arbitrary reads over the previous methodologies. The proposed hash index improves efficiency by reducing the amount of accessing data for search queries by creating buckets based on hyperplanes. The LSH hashes the input data where similar items with high probability maps to the same bucket. They attempt to decrease the volume of candidate data objects matched when reducing the missed nearest neighbors. The data space is divided with randomly chosen hyperplanes to decrease the volume of candidate objects. The values which are nearer to the boundaries (adjacent to the two sides of the hyperplane) are considered. The bucket label’s string length is equivalent to the amount of used hyperplanes. The effect of LSH for bucket size balancing and analysis of the non-indexed, hash index, and global-indexed dataset on MongoDB depicts the pre-eminence of the presented hash index.

Keywords:

• LSH
• buckets
• hyperplanes
• query
• document
• MongoDB

Additional information

Notes on contributors

N.R. Gayathiri

N R Gayathiri, assistant professor senior grade in the Department of Artificial intelligence and Data Science at Bannari Amman Institute of Technology. Her research interests include big data, AI and internet of things. Her research articles have appeared in the following forums: Journal of Computational and Theoretical Nanoscience, Concurrency and Computation Practice and Experience etc.

A.M. Natarajan

A M Natarajan, after completing his undergraduate degree (EEE) and post graduate degree (Applied Electronics) from PSG College of Technology, joined Guindy Engineering College (Now Anna University). He pursued his PhD under QIP at PSG College of Technology and received the degree in the year 1984. Later served at Government College of Technology for 14 years. After voluntary retirement, worked as Principal, Kongu Engineering College for 13 years, and then as chief executive at Bannari Amman Institute of Technology for 10 years and continuing as the chief executive at K P R Institute of Engineering and Technology, Coimbatore. Under his supervision, so far 30 candidates completed their PhD and at present 3 members are pursuing it. Email: [email protected]