Open Access

ARTICLE

A Novel Big Data Storage Reduction Model for Drill Down Search

N. Ragavan, C. Yesubai Rubavathi^*

Department of Computer Science and Engineering, Francis Xavier Engineering College, Anna University, Tamil Nadu, India

* Corresponding Author: C. Yesubai Rubavathi. Email:

Computer Systems Science and Engineering 2022, 41(1), 373-387. https://doi.org/10.32604/csse.2022.020452

Received 25 May 2021; Accepted 03 July 2021; Issue published 08 October 2021

Abstract

Multi-level searching is called Drill down search. Right now, no drill down search feature is available in the existing search engines like Google, Yahoo, Bing and Baidu. Drill down search is very much useful for the end user to find the exact search results among the huge paginated search results. Higher level of drill down search with category based search feature leads to get the most accurate search results but it increases the number and size of the file system. The purpose of this manuscript is to implement a big data storage reduction binary file system model for category based drill down search engine that offers fast multi-level filtering capability. The basic methodology of the proposed model stores the search engine data in the binary file system model. To verify the effectiveness of the proposed file system model, 5 million unique keyword data are stored into a binary file, thereby analysing the proposed file system with efficiency. Some experimental results are also provided based on real data that show our storage model speed and superiority. Experiments demonstrated that our file system expansion ratio is constant and it reduces the disk storage space up to 30% with conventional database/file system and it also increases the search performance for any levels of search. To discuss deeply, the paper starts with the short introduction of drill down search followed by the discussion of important technologies used to implement big data storage reduction system in detail.

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Keywords

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