An empirical comparison of the performances of single structure columnar in-memory and disk-resident data storage techniques using healthcare big data

Journal of Big Data

Research

A. O. Ibitoye¹  R. F. Famutimi¹  M. O. Oyelami¹  O. M. Awoniran¹ 

[1] Bowen University, Iwo, Nigeria;

关键词: Healthcare big data;    Disk-resident database;    Columnar in-memory-resident database;    Big data analytics;    Descriptive analysis;    Big data volume complexity;   

DOI  :  10.1186/s40537-023-00691-x

 received in 2022-04-20, accepted in 2023-01-21,  发布年份 2023

来源: Springer

【 摘 要 】

Healthcare data in images, texts and other unstructured formats have continued to grow exponentially while generating storage concerns. Even though there are other complexities, volume complexity is a major challenge for Disk-Resident technique in storage optimization. Hence, this research aimed to empirically compare the efficiency of Disk-Resident and In-Memory single structure database technique (as opposed to multiple structure In-Memory database), using descriptive and inferential big data analytical approaches. The essence was to discover a more cost-effective storage option for healthcare big data. Data from Nigerian Health Insurance Scheme (NHIS) alongside sample patients’ history from Made-in-Nigeria Primary Healthcare Information System (MINPHIS) which included patients’ investigation, patients’ bio-data and patients’ diagnoses were the primary data for this research. An implementation of both Disk-Resident and single structure In-Memory resident data storage was carried out on these big data sources. After storage, each quantity of data items stored for different data items in Disk-Resident was then compared with that of single structure In-Memory resident system using size of items as comparison criteria and different analyses made.The results obtained showed that single structure In-Memory technique conserved up to 90.57% of memory spaces with respect to the traditional (common) Disk-Resident technique for text data items. This shows that with this In-Memory technique, an improved performance in terms of storage was obtained.

【 授权许可】

CC BY   
© The Author(s) 2023

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