Title: Cloud storage framework for multivariate regression-based data mining: optimised LIFR and FIFR model

Authors: Saswati Sarkar; Anirban Kundu

Addresses: Computer Innovative Research Society, West Bengal – 711103, India ' Computer Innovative Research Society, West Bengal – 711103, India; Netaji Subhash Engineering College, Kolkata – 700152, India

Abstract: Authors propose a cloud-based storage framework to search data using optimised mapping. Multivariate regression-based data mining mechanism shows the utility of map tables, storage partitions, and data storage in cloud. The paper exhibits complexity of cloud-based searching algorithms in real-time scenario. The proposed cloud-based storage framework represents parallel disk searching technique to search data with less time consumption. Proposed technique exhibits linear time complexity. Drive partition concepts and map table concepts have been incorporated in this work for searching data in less time. It is observed that the overall execution time is inversely proportional to the number of drive partitions. Sequential and several parallel situations exhibit the comparisons using time graphs. The paper presents comparison graphs for execution time and time complexity between existing techniques and proposed approach with respect to storage partitions. Experimental observations and analysis using statistical data have been shown in this paper.

Keywords: optimised mapping; last in first read search; LIFR; first in first read search; FIFR; cloud storage; redundant array of independent drives; RAIDs; map table; multivariate regression.

DOI: 10.1504/IJWET.2025.145536

International Journal of Web Engineering and Technology, 2025 Vol.20 No.1, pp.66 - 99

Received: 14 Dec 2023
Accepted: 17 Nov 2024
Published online: 02 Apr 2025 *

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