|
Abstract.
The aim of this study is to develop an extensible microservice architecture for a data analysis software system, one that can dynamically scale and integrate various data processing algorithms. The proposed architecture, based on microkernel and microservice architectures, is demonstrated using a hyperspectral image analysis software system as an example, showcasing high efficiency and ease of incorporating new algorithms through modern software containerization approaches. The implemented software system demonstrates both the scalability and extensibility of the proposed architecture.
Keywords:
data analysis, microservice architecture, microkernel architecture, functional scaling, horizontal scaling, algorithm integration.
DOI 10.14357/20718632250307
EDN SQPKMV
PP. 73-85.
References
1. Sharma K, Giannakos M. Multimodal data capabilities for learning: What can multimodal data tell us about learning? British Journal of Educational Technology. 2020 Jul 4;51(5):1450–84. doi: 10.1111/bjet.12993. 2. Liu X, Iftikhar N, Xie X. Survey of real-time processing systems for big data. Proceedings of the 18th International Database Engineering & Applications Symposium on - IDEAS ’14. 2014. doi: 10.1145/2628194.2628251. 3. Rawat R, Yadav R. Big Data: Big Data Analysis, Issues and Challenges and Technologies. IOP Conference Series: Materials Science and Engineering. 2021 Jan 19;1022(1):012014. 4. Angel NA, Ravindran D, Vincent PMDR, Srinivasan K, Hu YC. Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies. Sensors. 2021 Dec 28;22(1):196. doi: 10.3390/s22010196. 5. Deng Z, Wang T, Zheng Y, Zhang W, Yun YH. Deep learning in food authenticity: Recent advances and future trends. Trends in Food Science and Technology. 2024 Feb 1;144:104344–4. doi: 10.1016/j.tifs.2024.104344. 6. Takahashi S, Sakaguchi Y, Nobuji Kouno, Takasawa K, Ishizu K, Akagi Y, et al. Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review. Journal of Medical Systems. 2024 Sep 12;48(1). doi: 10.1007/s10916-024-02105-8. 7. Patil R, Venkat Gudivada. A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs). Applied sciences (Basel). 2024 Mar 1;14(5):2074–4. doi: 10.3390/app14052074. 8. Blinowski G, Ojdowska A, Przybyłek A. Monolithic vs. Microservice Architecture: A Performance and Scalability Evaluation. IEEE Access [Internet]. 2022;10(2169-3536):20357–74. Available from: https://ieeexplore.ieee.org/abstract/document/9717259. doi: 10.1109/ACCESS.2022.3152803. 9. Haorongbam L, Nagpal R, Sehgal R. Service Oriented Architecture(SOA): A Literature Review on the Maintainability, Approaches and Design Process. 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence). 2022 Jan 27. doi: 10.1109/confluence52989.2022.9734153. 10. Manchana R. Event-Driven Architecture: Building Responsive and Scalable Systems for Modern Industries. International Journal of Science and Research (IJSR). 2021 Jan 5;10(1):1706–16. doi: 10.21275/sr24820051042. 11. Akaash Vishal Hazarika, Shah M. Serverless Architectures: Implications for Distributed System Design and Implementation. International Journal of Science and Research (IJSR). 2024 Dec 20;13(12):1250–3. doi: 10.21275/sr241216094817. 12. Sohail S. Plug-In based Software Architecture for the Development of Sustainable Software Ecosystem: Do’s and Don’ts. 13. Poniszewska-Marańda A, Czechowska E. Kubernetes Cluster for Automating Software Production Environment. Sensors. 2021 Mar 9;21(5):1910. doi: 10.3390/s21051910. 14. Carrión C. Kubernetes as a Standard Container Orchestrator - A Bibliometric Analysis. Journal of Grid Computing. 2022 Dec;20(4). doi: 10.1007/s10723-022-09629-8. 15. Sifat Ibtisum, Ehsan Bazgir, Syed Masiur Rahman, Syed Akhter Hossain. A comparative analysis of big data processing paradigms: Mapreduce vs. apache spark. World Journal Of Advanced Research and Reviews. 2023 Oct 30;20(1):1089–98. doi: 10.30574/wjarr.2023.20.1.2174. 16. Moritz P, Nishihara R, Wang S, Tumanov A, Liaw R, Liang E, et al. Ray: A Distributed Framework for Emerging AI Applications. arXiv (Cornell University). 2017 Dec 15. doi: 10.48550/arxiv.1712.05889. 17. Boettiger C. An introduction to Docker for reproducible research. ACM SIGOPS Operating Systems Review. 2015 Jan 20;49(1):71–9. doi: 10.1145/2723872.2723882. 18. Beetz F, Harrer S. GitOps: The Evolution of DevOps? IEEE Software. 2022;39(4):70–5. doi: 10.1109/ms.2021.3119106. 19. Birk RJ, McCord TB. Airborne hyperspectral sensor systems. IEEE Aerospace and Electronic Systems Magazine. 1994 Oct;9(10):26–33. doi: 10.1109/62.318881. 20. Saari H, Ville-Veikko Aallos, Anu Akujärvi, Antila T, Holmlund C, Uula Kantojärvi, et al. Novel miniaturized hyperspectral sensor for UAV and space applications. Proceedings of SPIE. 2009 Sep 17;7474. doi: 10.1117/12.830284. 21. Yu H, Kong B, Hou Y, Xu X, Chen T, Liu X. A critical review on applications of hyperspectral remote sensing in crop monitoring. Experimental Agriculture. 2022 Jan 1;58. doi: 10.1017/s0014479722000278. 22. Cheshkova AF. A review of hyperspectral image analysis techniques for plant disease detection and identif ication. Vavilov Journal of Genetics and Breeding [Internet]. 2022 Apr 5;26(2):202–13. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8983301/. doi: 10.18699/vjgb-22-25. 23. Zhang F, Zhou G. Estimation of vegetation water content using hyperspectral vegetation indices: a comparison of crop water indicators in response to water stress treatments for summer maize. BMC Ecology. 2019 Apr 29;19(1). doi: 10.1186/s12898-019-0233-0. 24. NIE J, WU K, LI Y, LI J, HOU B. Advances in hyperspectral remote sensing for precision fertilization decision-making: a comprehensive overview. TURKISH JOURNAL OF AGRICULTURE AND FORESTRY. 2024 Dec 2;48(6):1084–104. doi: 10.3390/rs13234832. 25. Schratz P, Muenchow J, Iturritxa E, Cortés J, Bischl B, Brenning A. Monitoring Forest Health Using Hyperspectral Imagery: Does Feature Selection Improve the Performance of Machine-Learning Techniques? Remote Sensing. 2021 Nov 28;13(23):4832. doi: 10.3390/rs13234832. 26. Thangavel K, Spiller D, Sabatini R, Amici S, Sasidharan ST, Fayek H, et al. Autonomous Satellite WildfireDetection Using Hyperspectral Imagery and Neural Networks: A Case Study on Australian Wildfire. Remote Sensing. 2023 Jan 26;15(3):720. doi: 10.3390/rs15030720. 27. Ye C, Li Y, Cui P, Liang L, Pirasteh S, Marcato J, et al. Landslide Detection of Hyperspectral Remote Sensing Data Based on Deep Learning With Constrains. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2019 Dec;12(12):5047–60. doi: 10.1109/jstars.2019.2951725. 28. Huang HY, Hsiao YP, Karmakar R, Mukundan A, Chaudhary P, Hsieh SC, et al. A Review of Recent Advances in Computer-Aided Detection Methods Using Hyperspectral Imaging Engineering to Detect Skin Cancer. Cancers [Internet]. 2023 Jan 1;15(23):5634. Available from: https://www.mdpi.com/2072-6694/15/23/5634. doi: 10.3390/cancers15235634. 29. Chiang N, Jain JK, Sleigh J, Vasudevan T. Evaluation of hyperspectral imaging technology in patients with peripheral vascular disease. Journal of Vascular Surgery. 2017 May 22;66(4):1192–201. doi: 10.1016/j.jvs.2017.02.047. 30. Vázquez-Ingelmo A, García-Holgado A, García-Peñalvo FJ. C4 model in a Software Engineering subject to ease the comprehension of UML and the software [Internet]. IEEE Xplore. 2020. p. 919–24. Available from: https://ieeexplore.ieee.org/abstract/document/9125335 31. Vinoski S. Advanced Message Queuing Protocol. IEEE Internet Computing. 2006 Nov;10(6):87–9. doi: 10.1109/mic.2006.116. 32. Zhang D, Lin F, Huang Y, Wang X, Zhang L. Detection of Wheat Powdery Mildew by Differentiating Background Factors using Hyperspectral Imaging. International Journal of Agriculture and Biology. 2016 Jul 1;18(04):747–56. doi: 10.17957/ijab/15.0162. 33. Gao B. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment. 1996 Dec;58(3):257–66. doi: 10.1016/s0034-4257(96)00067-3. 34. Huete AR. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment. 1988 Aug;25(3):295–309. doi: 10.1016/0034-4257(88)90106-x. 35. Paringer R, Mukhin A, Kupriyanov A. Formation of an informative index for recognizing specified objects in hyperspectral data. Computer Optics. 2021 Nov 1;45(6). doi: 10.18287/2412-6179-co-930. 36. Barreda-Ángeles M, Ioannis Arapakis, Bai X, B. Barla Cambazoglu, Alexandre Pereda-Baños. Unconscious Physiological Effects of Search Latency on Users and Their Click Behaviour. 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2015 Aug 9. 37. Arapakis I, Bai X, Cambazoglu BB. Impact of response latency on user behavior in web search. Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval - SIGIR ’14. 2014.
|
