Review Article
A Comparative Study of Parallel Processing, Distributed Storage Techniques, and Technologies: A Survey on Big Data Analytics
Saliha Mezzoudj*
,
Meriem Khelifa,
Yasmina Saadna
Issue:
Volume 10, Issue 5, October 2024
Pages:
86-99
Received:
29 September 2024
Accepted:
14 October 2024
Published:
12 November 2024
DOI:
10.11648/j.ijdsa.20241005.11
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Views:
Abstract: The significance of developing Big Data applications has increased in recent years, with numerous organizations across various industries relying more on insights derived from vast amounts of data. However, conventional data techniques and platforms struggle to cope the Big Data, exhibiting sluggish responsiveness and deficiencies in scalability, performance, and accuracy. In response to the intricate challenges posed by Big Data, considerable efforts have been made, leading to the creation of a range of distributions and technologies. This article addresses the critical need for efficient processing and storage solutions in the context of the ever-growing field of big data. It offers a comparative analysis of various parallel processing techniques and distributed storage frameworks, emphasizing their importance in big data analytics. Our study begins with definitions of key concepts, clarifying the roles and interconnections of parallel processing and distributed storage. It further evaluates a range of architectures and technologies, such as MapReduce, CUDA, Storm, Flink, MooseFS, and BeeGFS and others technologies, discussing their advantages and limitations in managing large-scale datasets. Key performance metrics are also examined, providing a comprehensive understanding of their effectiveness in big data scenarios.
Abstract: The significance of developing Big Data applications has increased in recent years, with numerous organizations across various industries relying more on insights derived from vast amounts of data. However, conventional data techniques and platforms struggle to cope the Big Data, exhibiting sluggish responsiveness and deficiencies in scalability, p...
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Research Article
Modelling Seasonal Variation and Lassa Fever Outbreak in Nigeria: A Predictive Approach
Issue:
Volume 10, Issue 5, October 2024
Pages:
100-108
Received:
14 October 2024
Accepted:
4 November 2024
Published:
29 November 2024
DOI:
10.11648/j.ijdsa.20241005.12
Downloads:
Views:
Abstract: Background: Lassa fever, a severe viral hemorrhagic fever caused by the Lassa virus, is a significant public health concern in West Africa, particularly in Nigeria. First identified in the 1950s, Lassa fever has been a persistent threat, causing outbreaks annually. This study investigates the temporal patterns and trends of Lassa fever outbreaks in Nigeria between 2017 and 2023, leveraging a comprehensive dataset from the Nigerian Centre for Disease Control (NCDC). Objective: The goal of this study is to analyze the seasonal variations and predict future occurrences of Lassa fever outbreaks in Nigeria. By employing the Box-Jenkins time series analysis and geo-spatial analysis, we aim to: Identify temporal patterns by Examining monthly and annual trends in Lassa fever case numbers, Forecast future outbreaks by utilizing an ARIMA model to predict future incidence rates and inform public health strategies by providing evidence-based recommendations to improve Lassa fever prevention and control efforts. Methods: This study utilized a secondary dataset comprising over 60 data points collected from the NCDC portal between 2017 and 2023. The Box-Jenkins time series analysis, specifically the ARIMA model, was employed to analyze the temporal patterns and forecast future trends. The model's adequacy was assessed using the Ljung-Box test. Additionally, geo-spatial analysis was conducted to visualize the spatial distribution of Lassa fever cases. Results: The analysis revealed distinct seasonal patterns in Lassa fever incidence, influenced by Nigeria's climatic and environmental conditions. Monthly fluctuations in confirmed cases were observed, with peak periods aligning with specific seasons. The ARIMA (0, 1, 1)(0, 1, 1)12 model demonstrated a strong fit to the data, providing reliable forecasts for future outbreaks. Conclusion: This study underscores the importance of strengthening surveillance systems for early detection and rapid response to Lassa fever outbreaks, particularly during peak seasons. Implementing effective rodent control measures, promoting good hygiene practices, and improving environmental sanitation are crucial for reducing the risk of Lassa fever transmission. Furthermore, enhancing collaboration between government agencies, healthcare providers, and research institutions is essential for optimizing Lassa fever prevention and control efforts.
Abstract: Background: Lassa fever, a severe viral hemorrhagic fever caused by the Lassa virus, is a significant public health concern in West Africa, particularly in Nigeria. First identified in the 1950s, Lassa fever has been a persistent threat, causing outbreaks annually. This study investigates the temporal patterns and trends of Lassa fever outbreaks in...
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