International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Suprihadi 1*, Sutarto Wijono 2, Kristoko Dwi Hartomo 1

1 Department of Computer Science, Faculty of Information Technology, Satya Wacana Christian University, Salatiga, Indonesia

2 Department of Psychology, Faculty of Psychology, Satya Wacana Christian University, Salatiga, Indonesia


 

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ABSTRACT


The field of Information Service Management is concerned with the effective management of information technology (IT) services across their entire lifespan, encompassing activities such as design, development, deployment, operation, and continuous improvement. The components encompass many procedures, namely service design, service transition, service operation, and constant service improvement. The proliferation of information service management aligns with the ubiquity of the internet and its web-based services, notwithstanding the constraints posed by intermittent wireless connectivity. When the attainment of web service reliability is accomplished, there is a decrease in communication overhead, resulting in the retrieval of the correct response with little exertion. The worldwide pandemic issue presents an opportunity to glean useful insights that can contribute to the progress of digital health technologies. The remote monitoring of patient's health conditions and treatment actions inside the health system is crucial to mitigate the potential danger of disease transmission. Furthermore, there is a need for further development of digital technology that enables the sharing of information and facilitates evidence-based decision-making among stakeholders. The provision of data integration and analysis services is necessitated by the utilization of web services. The utilization of the middleware technique is deemed to be more suitable for web services. This study presents a novel model, referred to as Analysis Service Architecture utilizing Middleware (ASAM). The ASAM framework was established through the utilization of the Service Computing Systems Engineering Life Cycle approach. Further, ASAM is applied to the Management of Tuberculosis Information Services in Epidemic Tuberculosis cases in Indonesia. We evaluate and analyze our proposed method with other Information service management including face recognition, object recognition, and optical character recognition as a basis for evidence-based decision-making. Black box method testing with 50 service samples was carried out to measure aspects of functionality, reliability, and efficiency with ISO/ICE 9126 standards. The result is that the ASAM model is considered feasible as a new model of service-oriented middleware based on service-oriented architecture.


Keywords: Service architecture, ASAM, Information service, Artificial intelligence.


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REFERENCES


  1. Abdelfattah, A.S., Abdelkader, T., EI-Horbaty, E.S.M. 2020. RAMWS: Reliable approach using middleware and WebSockets in mobile cloud computing. Ain Shams Engineering Journal, 11, 1083–1092.

  2. Abdelfattah, A.S., Abdelkader, T., EI-Horbaty, E.S.M. 2018. RSAM: An enhanced architecture for achieving web services reliability in mobile cloud computing. Journal of King Saud University-Computer and Information Sciences, 30, 164–174.

  3. Alshamrani, M. 2022. IoT and artificial intelligence implementations for remote healthcare monitoring systems: A survey. Journal of King Saud University- Computer and Information Sciences, 34, 4687–4701.

  4. Ardhianto, P., Liau, B.Y., Jan, Y.K., Tsai, J.Y., Akhyar, F., Lin, C.Y., Subiakto R.B.R., Lung, C.W. 2022. Deep learning in left and right footprint image detection based on plantar pressure. Applied Sciences, 12, 8885.

  5. Bajaj, K., Jain, S., Singh, R. 2023. Context-aware offloading for iot application using fog-cloud computing. International Journal of Electrical and Electronics Research, 11, 69–83.

  6. Balasubramanian, N., Gurumurthy, T.R., Bharat, D. 2020. Receiver based contention management: A cross layer approach to enhance performance of wireless networks. Journal of King Saud University-Computer and Information Sciences, 32, 1117–1126.

  7. Calimeri, F., Cauteruccio, F., Cinelli, L.U.C.A., Marzullo, A., Stamile, C., Terracina, G., Durand-Dubief, F., Sappey-Marinier, D. 2019. A logic-based framework leveraging neural networks for studying the evolution of neurological disorders. Theory and Practice of Logic Programming, 21, 80–124.

  8. Cao, L. 2022. AI in finance: Challenges, techniques, and opportunities. ACM Computing Surveys, 55.

  9. Castelli, M., Manzoni, L., Espindola, T., Popovič, A., De Lorenzo, A. 2021. Generative adversarial networks for generating synthetic features for Wi-Fi signal quality. PLoS ONE, 16, e0260308.

  10. Champaneria, T., Jardosh, S., Makwana, A. 2022. Microservices in IoT middleware architectures: Architecture, trends, and challenges. IOT with Smart Systems: Proceedings of ICTIS 2022, 2, 381–395.

  11. Chen, Y., Lin, C., Huang, J., Xiang, X., Shen, X. 2017. Energy efficient scheduling and management for large-scale services computing systems. IEEE Transactions on Services Computing, 10, 217–230.

  12. Dewi, C., Chen, A.P.S., Christanto, H.J. 2023a. Deep learning for highly accurate hand recognition based on Yolov7 model. Big Data and Cognitive Computing, 7, 53.

  13. Dewi, C., Chen, A.P.S., Christanto, H.J. 2023b. Recognizing Similar musical instruments with YOLO models. Big Data and Cognitive Computing, 7, 94.

  14. Dewi, C. Chen, R.-C. 2022. Automatic medical face mask detection based on cross-stage partial network to combat COVID-19. Big Data and Cognitive Computing, 6, 106.

  15. Dewi, C., Chen, R.-C., Zhuang, Y.-C., Jiang, X., Yu, H. 2023c. Recognizing road surface traffic signs based on Yolo models considering image flips. Big Data and Cognitive Computing, 7, 54.

  16. Dewi, C., Chen, R.C., Yu, H., Jiang, X. 2021. Robust detection method for improving small traffic sign recognition based on spatial pyramid pooling. Journal of Ambient Intelligence and Humanized Computing, 12, 1–18.

  17. Dewi, C., Chen, A.P.S., Christanto, H.J. 2023d. YOLOv7 for face mask identification based on deep learning. 2023 15th International Conference on Computer and Automation Engineering (ICCAE), 193–197.

  18. Falzon, D., Migliori, G.B., Jaramillo, E., Weyer, K., Joos, G., Raviglione, M. 2017. Digital health to end tuberculosis in the Sustainable Development Goals era: Achievements, evidence and future perspectives. European Respiratory Journal, 50.

  19. Findi, M. 2021. Aplikasi Digital dalam Mendukung Layanan Kesehatan untuk Eliminasi Tuberkulosis di Indonesia.

  20. França, J.M., Soares, M.S. 2015. SOAQM: Quality model for SOA Applications based on ISO 25010. In ICEIS, 60–70.

  21. Gamal, I., Abdel-Galil, H. Ghalwash, A. 2022. Osmotic message-oriented middleware for Internet of Things. Computers, 11, 56.

  22. Giao, J., Nazarenko, A. A., Luis-Ferreira, F., Gonçalves, D., Sarraipa, J. 2022. A framework for service-oriented architecture (SOA)-based IoT application development. Processes, 10, 1782.

  23. Huang, J., Lin, C., Kong, X., Wei, B., Shen, X. 2014. Modeling and analysis of dependability attributes for services computing systems. IEEE Transactions on Services Computing, 7, 599–613.

  24. Huang, J., Lin, C., Wan, J. 2013. Modeling, analysis and optimization of dependability-aware energy efficiency in services computing systems. Proceedings - IEEE 10th International Conference on Services Computing, SCC, 683–690.

  25. Kuhn Cuellar, L., Friedrich, A., Gabernet, G., de la Garza, L., Fillinger, S., Seyboldt, A., Koch, T., zur Oven-Krockhaus, S., Wanke, F., Richter, S., Thaiss, W.M., Horger, M., Malek, N., Harter, K., Bitzer, M., Nahnsen, S. 2022. A data management infrastructure for the integration of imaging and omics data in life sciences. BMC Bioinformatics, 23, 61.

  26. Lamnaour, M., Begdouri, M.A., Mesmoudi, Y., El Khamlichi, Y., Tahiri, A. 2022. A semantic MSOAH-IoT design for improving efficiency and solving heterogeneity within IoT applications. Journal of Communications, 17.

  27. Lee, Y., Raviglione, M.C., Flahault, A. 2020. Use of digital technology to enhance tuberculosis control: Scoping review. Journal of Medical Internet Research, 22, 1–15.

  28. Lyu, G., Liu, P., Lu, Y., Wang, T., Kang, X. 2021. A data middle platform architecture based on microservice serving power grid business. 2021 8th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2021 7th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom), 219–224.

  29. Mahmoud, Q.H. 2004. Middleware for communications, 73. John Wiley & Sons.

  30. Mesmoudi, Y., Lamnaour, M., El Khamlichi, Y., Tahiri, A., Touhafi, A., Braeken, A. 2020. A middleware based on service oriented architecture for heterogeneity issues within the Internet of Things (MSOAH-IoT). Journal of King Saud University-Computer and Information Sciences, 32, 1108–1116.

  31. Mohamed, N., Al-Jaroodi, J., Jawhar, I., Lazarova-Molnar, S., Mahmoud, S. 2017. SmartCityWare: A service-oriented middleware for cloud and fog enabled smart city services. IEEE Access, 5, 17576–17588.

  32. Naseer, A., Alkazemi, B.Y., Aldoobi, H.I. 2016. A general-purpose service-oriented middleware model for WSN. International Conference on Ubiquitous and Future Networks, ICUFN, 283–287.

  33. Nayak, N., Ambalavanan, U., Thampan, J.M., Grewe, D. 2023. Reimagining automotive service-oriented communication: A case study on programmable data planes. IEEE Vehicular Technology Magazine, 2, 2–12.

  34. Ngwatu, B.K., Nsengiyumva, N.P., Oxlade, O., Mappin-Kasirer, B., Nguyen, N.L., Jaramillo, E., Falzon, D., Schwartzman, K. 2018. The impact of digital health technologies on tuberculosis treatment: A systematic review. European Respiratory Journal, 51.

  35. Phuttharak, J., Loke, S.W. 2023. An event-driven architectural model for integrating heterogeneous data and developing smart city applications. Journal of Sensor and Actuator Networks, 12, 12.

  36. Prisilla, A.A., Guo, Y.L., Jan, Y.K., Lin, C.Y., Lin, F.Y., Liau, B.Y., Tsai, J.Y., Ardhianto, P., Pusparani, Y., Lung, C.W. 2023. An approach to the diagnosis of lumbar disc herniation using deep learning models. Frontiers in Bioengineering and Biotechnology, 11.

  37. Pusparani, Y., Lin, C.Y., Jan, Y.K., Lin, F.Y., Liau, B.Y., Ardhianto, P., Farady, I., Alex, J.S.R., Aparajeeta, J., Chao, W.H., Lung, C.W. 2023. Diagnosis of Alzheimer’s disease using convolutional neural network with select slices by landmark on hippocampus in MRI images. IEEE Access, 11.

  38. Riono, P. 2019. Tantangan Kita Mencapai Eliminasi Tuberkulosis di Indonesia tahun 2030. Jurnal Kesehatan, 2–9.

  39. Rohmah, R.N., Handaga, B., Nurokhim, N., Soesanti, I. 2019. A statistical approach on pulmonary tuberculosis detection system based on X-ray image. Telkomnika (Telecommunication Computing Electronics and Control), 17, 1474–1482.

  40. Sathis Kumar, T., Latha, K. 2020. Middleware interoperability performance using interoperable reinforcement learning technique for enterprise business applications. Journal of Intelligent & Fuzzy Systems, 38.

  41. Sembiring, J., Uluwiyah, A. 2015. Data exchange design with SDMX format for interoperability statistical data. TELKOMNIKA Indonesian Journal of Electrical Engineering, 14, 343–352.

  42. Sharif, Z., Jung, L.T., Ayaz, M., Yahya, M., Pitafi, S. 2023. Priority-based task scheduling and resource allocation in edge computing for health monitoring system. Journal of King Saud University-Computer and Information Sciences, 35, 544–559.

  43. Suhardi, N.K., Sembiring, J. 2017. Service computing system engineering life cycle. Proceeding of the Electrical Engineering Computer Science and Informatics, 4, 347–352.

  44. Sukarsa, I., Wisswani, N.W., Wirabuana, P. 2014. Data exchange between information system at low bandwidth quality using messaging. Journal of Theoretical and Applied Information Technology, 60, 417–422.

  45. Suprihadi, S., Wijono, S., Hartomo, K.D. 2020. Service oriented middleware for tuberculosiss information services management. 2020 International Seminar on Application for Technology of Information and Communication (iSemantic), 425–430.

  46. Teixeira, T., Hachem, S., Issarny, V., Georgantas, N. 2011. Service oriented middleware for the internet of things: A perspective. In European conference on a service-based internet, 220–229.

  47. Tjung, Y., Wibowo, A., Ridha, M.A.F. 2020. A model and implementation of academic data integration in near-real time using message-oriented middleware to support analysis of student performance in the information technology department of Politeknik Caltex Riau. JUTI: Jurnal Ilmiah Teknologi Informasi, 18, 9–18

  48. Wang, N., Zhang, H., Zhou, Y., Jiang, H., Dai, B., Sun, M., Li, Y., Kinter, A., Huang, F. 2019. Using electronic medication monitoring to guide differential management of tuberculosis patients at the community level in China. BMC Infectious Diseases, 19, 1–9.

  49. Wisswani, N.W., Wijaya, I.W.K. 2018. Message oriented middleware for library’s metadata exchange. Telkomnika (Telecommunication Computing Electronics and Control), 16, 2756–2762.

  50. World Health Organization. 2017. Handbook for the use of digital technologies to support tuberculosis medication adherence.

  51. World Health Organization. 2020. Global tuberculosis report 2020. World Health Organization, Geneva.

  52. World Health Organization. 2021. Global tuberculosis report 2021. Geneva.

  53. Wu, Z. 2014. Service computing: Concept, method and technology. Academic Press.

  54. Ye, D., He, Q., Wang, Y., Yang, Y. 2019. An Agent-based integrated self-evolving service composition approach in networked environments. IEEE Transactions on Services Computing, 12, 880–895.

  55. Zhang, Y., Tang, D., Zhu, H., Zhou, S., Zhao, Z. 2022. An efficient IIoT gateway for cloud–edge collaboration in cloud manufacturing. Machines, 10.


ARTICLE INFORMATION


Received: 2023-07-17
Revised: 2023-10-13
Accepted: 2023-12-11
Available Online: 2024-01-05


Cite this article:

Suprihadi, Wijono, S., Hartomo, K.D. 2024. Analysis service architecture utilizing middleware for information services management systems. International Journal of Applied Science and Engineering, 21, 2023273. https://doi.org/10.6703/IJASE.202406_21(2).001

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