Effective Data Management Using Iterative Approach in Data Systems
J Albert Sagaya David () and
G Dhivya ()
SPAST Reports, 2024, vol. 1, issue 3
Abstract:
This study explored memory management in large datasets from a user-centric perspective, filling a research gap often overlooked. While previous projects primarily aimed to improve data maintenance techniques, this research sought to scrutinize the process of data storage and management within these systems. The primary objective was to identify and analyze the issues encountered throughout the stages of the public tendering process and present potential solutions. The existing system faced application performance issues, bid submission delays, and complexities in bid evaluation, often due to a lack of clarity in the scope of work. In response, the proposed system introduces a user-friendly auction platform with enhanced data management capabilities, catering to sellers, bidders, and merchants. It streamlines sensitive data handling, bidding records, and transactions while employing a divide-and-iterate approach for improved efficiency. This study's contribution lies in addressing the critical challenges in online bidding processes and offering innovative solutions for enhanced performance and data management, with future potential for blockchain and smart contract integration.
Keywords: Data Systems; Data Management; Divide-and-Iterate; Design Document Specification; SRS (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:
Downloads: (external link)
https://spast.org/article/view/4813/385 (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:bps:jspath:v:1:y:2024:i:3:id:4813
Access Statistics for this article
SPAST Reports is currently edited by Srinesh Singh Thakur
More articles in SPAST Reports from SPAST Foundation
Bibliographic data for series maintained by Srinesh Singh Thakur ().