Heterojen Medikal IoT Verilerinin Depolanmasında İlişkisel Olmayan Veritabanına Dayalı Bir Yaklaşım

Yıl 2019, , 989 - 998, 01.12.2019

Hüseyin Polat , Saadin Oyucu

https://doi.org/10.2339/politeknik.477311

Cited By: 2

Öz

Modern medikal cihazlar, her türlü
temel medikal veriyi üretme ve iletebilme kabiliyetine sahip olmuşlardır. Bu
cihazlar, birbirleriyle veri paylaşabilir veya bulutta merkezi bir platforma
veri gönderebilir. Sağlık endüstrisinde yeni trend, her zaman ve her yerden
erişilebilecek şekilde, buluttan sunulan elektronik medikal kayıtlarla entegre
bir tıbbi izleme sisteminin oluşturulmasıdır. Hacmi gittikçe artan heterojen
medikal verilerin düşük maliyetle, hızlı ve güvenli bir şekilde veritabanı
sisteminde depolanması, verilerin aktarılması, paylaşılması ve
görselleştirilmesi esastır. Bu çalışmada heterojen medikal verileri
algılayıcılardan toplamak, verileri görselleştirmek ve depolamak için farklı
veritabanı sistemlerini kullanabilecek şekilde bir medikal Nesnelerin İnterneti
(medical Internet of Things – mIoT) platformu gerçekleştirilmiştir. mIoT
platformu üzerinde dört farklı veritabanı modeli dört farklı senaryo ile test
edilmiştir. Bu senaryolarda mIoT platformunda kullanılan veritabanı
modellerinin performansları; sorgu süresi, veri hazırlığı, esneklik, güvenlik
ve ölçeklenebilirlik parametreleri göz önüne alınarak karşılaştırılmıştır. mIoT
platformunda kullanılan ilişkisel olmayan veritabanı modelinin (NoSQL: Not only
Structured Query Language) okuma/yazma işlemlerinde ilişkisel veritabanı
modellerine göre daha verimli çalıştığı, performansının, esnekliğinin ve
ölçeklenebilirliğinin ilişkisel veritabanı sistemlerine göre daha iyi olduğu
gözlemlenmiştir.

Anahtar Kelimeler

Medikal IoT, SQL, veritabanı performansı, NoSQL veritabanları

An Approach to Non-Relational Database-Based in the Storing of Heterogeneous Medical IoT Data

Öz

Modern medical devices have the ability to producing
and transmitting all kinds of basic medical data. These devices can share data
with each other or send data to a central platform in the cloud. The new trend
in the healthcare industry is the creation of a medical monitoring system
integrated with electronic medical records that presented from the cloud, that
can be accessed anytime and anywhere. It is essential to store, transfer, share
and visualize large volume heterogeneous medical data in a low-cost, fast and
secure database system. In this study, a medical Internet of Things (mIoT) platform
that can use different database systems was realized in order to acquisition
heterogeneous medical data from sensors to visualize and store data. Four
different database models on mIoT platform were tested with four different
scenarios. The performance of the database models used in the mIoT platform in
these scenarios; query time, data preparation, flexibility, security and
scalability parameters were compared. It was observed that the non-relational
database model (NoSQL: Not only Structured Query Language) used on the mIoT
platform was more efficient in reading/writing operations than relational
database models, and its performance, flexibility and scalability were better
than relational database systems.

Anahtar Kelimeler

Medical IoT, database performance, SQL, NoSQL databases

Kaynakça

• P. C. Evans and M. Annunziata, “Industrial Internet: Pushing the Boundaries of Minds and Machines”, 2012.
• S. F. Khan, “Health care monitoring system in Internet of Things (IoT) by using RFID”, 2017 6th Int. Conf. Ind. Technol. Manag, Cambridge, UK, pp. 198–204, 2017.
• S. H. Almotiri, M. A. Khan, and M. A. Alghamdi, “Mobile health (m-Health) system in the context of IoT”, 2016 4th Int. Conf. Futur. Internet Things Cloud Work. W-FiCloud, Vienne, Austria, pp. 39–42, 2016.
• Z. Goli-Malekabadi, M. Sargolzaei-Javan, and M. K. Akbari, “An effective model for store and retrieve big health data in cloud computing”, Comput. Methods Programs Biomed, vol. 132, pp. 75–82, 2016.
• Y. Kang, I. Park, J. Rhee, and Y. Lee, “MongoDB-based Repository Design for IoT- generated RFID / Sensor Big Data,” IEEE Sens. J., vol. 16, no. 2, pp. 485–497, 2016.
• A. Archip, N. Botezatu, E. SSerban, P.-C. Herghelegiu, and A. Zal, “An IoT based system for remote patient monitoring”, 17th Int. Carpathian Control Conf. ICCC, Tatras, Slovakia, pp. 1–6, 2016.
• J. Cruz, D. Brooks, and A. Marques, “Home telemonitoring in COPD: A systematic review of methodologies and patients’ adherence”, Int. J. Med. Inform., vol. 83, no. 4, pp. 249–263, 2014.
• L. Pollonini, N. O. Rajan, S. Xu, S. Madala, and C. C. Dacso, “A novel handheld device for use in remote patient monitoring of heart failure patients-design and preliminary validation on healthy subjects”, J. Med. Syst., vol. 36, no. 2, pp. 653–659, 2012.
• Y. Msayib, P. Gaydecki, M. Callaghan, N. Dale, and S. Ismail, “An Intelligent Remote Monitoring System for Total Knee Arthroplasty Patients”, J. Med. Syst., vol. 41, no. 6, pp. 1–6, 2017.
• D. Sotiriou, “Health Care in Remote Areas,” no. 1, pp. 69–76, 1995.
• S. Kumar, A. Bansal, V. N. Tiwari, M. M. Nayak, and R. V. Narayanan, “Remote health monitoring system for detecting cardiac disorders”, IEEE Int. Conf. Bioinforma. Biomed. IEEE BIBM, Belfast, UK, pp. 30–34, 2014.
• M. A. Harbawi, M. I. Ibrahimy, and S. M. A. Motakabber, “Photoplethysmography based remote health monitoring system”, IEEE Int. Conf. Smart Instrumentation, Meas. Appl. ICSIMA, Kuala Lumpur, Malaysia, no. November, pp. 26–27, 2013.
• R. Jameson, D. Lorence, and J. Lin, “Data capture of transdermal glucose monitoring through computerized appliance-based virtual remote sensing and alert systems”, J. Med. Syst., vol. 36, no. 4, pp. 2193–2201, 2012.
• S. Babu, M. Chandini, P. Lavanya, K. Ganapathy, and V. Vaidehi, “Cloud-enabled remote health monitoring system”, Recent Trends Inf. Technol. (ICRTIT), Chenna, India, pp. 702–707, 2013.
• R. N. Kirtana and Y. V. Lokeswari, “An IoT based remote HRV monitoring system for hypertensive patients”, Comput. Commun. Signal Process. Spec. Focus IoT, ICCCSP, Tamilnadu, India, 2017.
• A. Al-Adhab, H. Altmimi, M. Alhawashi, H. Alabduljabbar, F. Harrathi, and H. ALmubarek, “IoT for remote elderly patient care based on Fuzzy logic”, Networks, Comput. Commun., Hawaii, USA, pp. 1–5, 2016.
• W. Raghupathi and V. Raghupathi, “Big data analytics in healthcare: promise and potential”, Heal. Inf. Sci. Syst., vol. 2, no. 1, p. 3, 2014.
• K. Xiaomin, B. Fan, W. Nie, and D. Yi, “Design on mobile health service system based on Android platform”, IEEE Adv. Inf. Manag. Commun. Electron. Autom. Control Conf. IMCEC, Xi'an, China, pp. 1683–1687, 2017.
• A. Degada and V. Savani, “Design and implementation of low cost, portable telemedicine system: An embedded technology and ICT approach”, NUiCONE 2015 - 5th Nirma Univ. Int. Conf. Eng., Ahmedabad, India, 2016.
• S. N. Sharwardy, Z. Rahman, S. Parveen, H. Sarwar, and A. M. Hossain, “A cost-effective web-based teleconsultation system”, 8th Int. Conf. Inf. Technol. Asia - Smart Devices Trend Technol. Futur. Lifestyle, Proc. CITA, Xi'an, China, no. c, pp. 2–5, 2013.
• P. Hu, H. Ning, T. Qiu, Y. Xu, X. Luo, and A. K. Sangaiah, “A unified face identification and resolution scheme using cloud computing in Internet of Things”, Futur. Gener. Comput. Syst., vol. 81, pp. 582–592, 2018.
• A. Maurya and D. S. Bade, “Design of a wireless health monitoring system based on M2M communication”, Int. Conf. Control. Instrumentation, Commun. Comput. Technol. ICCICCT, Kanyakumari, India, pp. 949–953, 2014.
• I. T. Pambudi, T. Hayasaka, K. Tsubota, S. Wada, T. Yamaguchi, and Ieee, “Sustainable patient information network (SPaIN) for primary care health center in Indonesia”, 25th Annu. Int. Conf. Ieee Eng. Med. Biol. Soc. Vols 1-4 A New Begin. Hum. Heal., vol. 25, pp. 1421–1424, 2003.
• M. Sanborn, “Director’s Forum - Developing a Pharmacy Information System Infrastructure”, Hosp. Pharm., vol. 42, no. 5, pp. 470–473, 2007.
• A. Hussain, R. Wenbi, A. L. Da Silva, M. Nadher, and M. Mudhish, “Health and emergency-care platform for the elderly and disabled people in the Smart City”, J. Syst. Softw., vol. 110, pp. 253–263, 2015.
• A. Talaminos-Barroso, M. A. Estudillo-Valderrama, L. M. Roa, J. Reina-Tosina, and F. Ortega-Ruiz, “A Machine-to-Machine protocol benchmark for eHealth applications - Use case: Respiratory rehabilitation”, Comput. Methods Programs Biomed., vol. 129, pp. 1–11, 2016.
• P. Paethong, M. Sato, and M. Namiki, “Low-power distributed NoSQL database for IoT middleware”, 5th ICT Int. Student Proj. Conf. ICT-ISPC, Nakhon Pathom, Thailand, pp. 158–161, 2016.
• A. J. Poulter, S. J. Johnston, and S. J. Cox, “Using the MEAN stack to implement a RESTful service for an Internet of Things application”, IEEE World Forum Internet Things, Milan, Italy, pp. 280–285, 2015.
• B. Farahani, F. Firouzi, V. Chang, M. Badaroglu, N. Constant, and K. Mankodiya, “Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare”, Futur. Gener. Comput. Syst., vol. 78, pp. 659–676, 2018.
• L. Nkenyereye and J. W. Jang, “Performance Evaluation of Server-side JavaScript for Healthcare Hub Server in Remote Healthcare Monitoring System”, Procedia Comput. Sci., vol. 58, no. Icth, pp. 382–387, 2016.
• J. S. van der Veen, B. van der Waaij, and R. J. Meijer, “Sensor Data Storage Performance: SQL or NoSQL, Physical or Virtual”, IEEE Fifth Int. Conf. Cloud Comput., Nice, France, pp. 431–438, 2012.
• Z. Wei-Ping, L. Ming-Xin, and C. Huan, “Using MongoDB to implement textbook management system instead of MySQL”, IEEE 3rd Int. Conf. Commun. Softw. Networks, ICCSN, Xi'an, China, pp. 303–305, 2011.
• C. Gyorodi, R. Gyorodi, G. Pecherle, and A. Olah, “A comparative study: MongoDB vs. MySQL”, 13th Int. Conf. Eng. Mod. Electr. Syst. EMES, Oradea, Romania, pp. 0–5, 2015.