@article{ART003198702},
author={Hyun-Seob Lee},
title={Data Mining-Based Data Access Address Analysis for Data Management fficiency},
journal={Journal of Internet of Things and Convergence},
issn={2466-0078},
year={2025},
volume={11},
number={2},
pages={105-110}
TY - JOUR
AU - Hyun-Seob Lee
TI - Data Mining-Based Data Access Address Analysis for Data Management fficiency
JO - Journal of Internet of Things and Convergence
PY - 2025
VL - 11
IS - 2
PB - The Korea Internet of Things Society
SP - 105
EP - 110
SN - 2466-0078
AB - With rapid advances in storage technology, flash memory-based storage is replacing traditional hard disk drives and emerging as a high-performance storage solution. SSD, in particular, are widely adopted in data centers, cloud computing, and mobile devices because of their fast access speeds and low power consumption. However, the media in SSD has unique characteristics of flash memory, including asymmetrical read and write performance and the need to write and erase data. Therefore, efficient data management is critical to maximize the performance and lifespan of SSD, which requires an understanding of their internal data structures and access patterns. In particular, identifying frequently accessed data patterns in randomly used data is expected to significantly improve storage system performance, extend SSD lifetime, and enable effective data management strategies. Therefore, this research aims to analyze the access patterns of SSD to improve data management efficiency. To achieve this, we use data mining techniques to extract and analyze meaningful patterns from a large dataset of SSD access addresses. The study first visualizes SSD access addresses to identify access patterns, analyzes the frequency and distribution of frequent accesses, and proposes a predictive model to optimize performance and data management strategies. The proposed methodology provides a systematic approach to analyze SSD data structure and access patterns through data mining, which can be applied to a wide range of storage, and the results of the study can be applied to real-world systems to optimize storage and support the development of effective data management solutions.
KW - Data Analysis;Data Mining;Access Frequency;Data Pattern;Flash Memory
DO -
UR -
ER -
Hyun-Seob Lee. (2025). Data Mining-Based Data Access Address Analysis for Data Management fficiency. Journal of Internet of Things and Convergence, 11(2), 105-110.
Hyun-Seob Lee. 2025, "Data Mining-Based Data Access Address Analysis for Data Management fficiency", Journal of Internet of Things and Convergence, vol.11, no.2 pp.105-110.
Hyun-Seob Lee "Data Mining-Based Data Access Address Analysis for Data Management fficiency" Journal of Internet of Things and Convergence 11.2 pp.105-110 (2025) : 105.
Hyun-Seob Lee. Data Mining-Based Data Access Address Analysis for Data Management fficiency. 2025; 11(2), 105-110.
Hyun-Seob Lee. "Data Mining-Based Data Access Address Analysis for Data Management fficiency" Journal of Internet of Things and Convergence 11, no.2 (2025) : 105-110.
Hyun-Seob Lee. Data Mining-Based Data Access Address Analysis for Data Management fficiency. Journal of Internet of Things and Convergence, 11(2), 105-110.
Hyun-Seob Lee. Data Mining-Based Data Access Address Analysis for Data Management fficiency. Journal of Internet of Things and Convergence. 2025; 11(2) 105-110.
Hyun-Seob Lee. Data Mining-Based Data Access Address Analysis for Data Management fficiency. 2025; 11(2), 105-110.
Hyun-Seob Lee. "Data Mining-Based Data Access Address Analysis for Data Management fficiency" Journal of Internet of Things and Convergence 11, no.2 (2025) : 105-110.
