RELATIONAL DATABASE OF INTELLIGENT AUTOMATED MICROSCOPY SYSTEM

O. M. Berezky, O. Yo. Pitsun, S. O. Verbovyi, T. V. Datsko

Abstract


Currently in medicine researchers pay much attention to designing databases for information systems that facilitate the work of doctors. Basically, the structure of a relational database allows conveniently generating reports and statistics on patients and their diagnoses. Most existing automated microscopy systems for image analysis do not have some kind of database or have limited functionality. In this paper, based on an analytical approach to the review of existing solutions for the design databases of information systems and based on the theory of database design created a model of a relational database for automated microscopy system that allowed developing adaptive functionality for different types of users. Developed intelligent automated microscopy system includes graphical interfaces and functionality for these types of users: doctor, doctor – diagnostician, expert, and administrator. A feature of this system is a mechanism of information exchange between doctors. For example, the doctor conducts image processing, and the doctor – diagnostician carries out a detailed analysis of the same set of images. In case of doubtful situations, doctors can contact the expert and ask his opinion. The system is designed so that doctors do not need to know all the technical nuances associated with the operation of the database and FTP – server to store images. Stored data securing is an important concern, especially in medicine. This system is suggested to use the mechanism of data replication type "mater-slave". This approach allows distributing the load between servers and backing up data. That is, when damaged one of the servers, the system automatically switches to the other. Given the fast growing popularity of non-relational database and increasing amounts of information about patients and their research raises the urgent task of developing software modules using non-relational databases (mongoDB) to speed read / write data.


Keywords


replication; FTP-server; datalogic model; histological and cytological images, master-slave

Full Text:

PDF

References


Alper, S. B., Stevermer, J. J., White, D. S., & Ewigman, B. G. (2001). Answering family physicians clinical questions using electronic medical databases. Journal of Family Practice, 50(11), 960–965.

Berezkyi, O. M., Verbovyi, S. O., & Pitsun, O. Y. (2016). Systemy avtomatyzovanoi mikroskopii: stan ta perspektyvy rozvytku. Visnyk Khmelnytskoho natsionalnoho universytetu, 2(235), 61–68. [in Ukrainian].

Berezsky, O., Dubchak, L., & Pitsun, O. (2017). Access distribution in automated microscopy system. In The Experience of Designing and Application of Cad Systems in Microelectronics, CADSM2017. Poliana-Svaliava, 360 p.

Chang, F., Dean, J., Ghemawat, S., & Hsieh, W. C. (2008). Bigtable: A Distributed Storage System for Structured Data. ACM Transactions on Computer Systems (TOCS), 26(2), 1–26. https://doi.org/10.1145/1365815.1365816

Delgado, M., SáNchez, D., MartıN-Bautista, M. J., & Vila, M. A. (2001). Mining association rules with improved semantics in medical databases. Artificial Intelligence in Medicine, 21(1), 241–245. https://doi.org/10.1016/S0933-3657(00)00092-0

Goldacre, M., Kurina, L., Yeates, D., Seagroatt, V., & Gill, L. (2000). Use of large medical databases to study associations between diseases. QJM, 93(10), 669–675. https://doi.org/10.1093/qjmed/93.10.669

Pacitti, E., Özsu, M. T., & Coulon, C. (2003) Preventive Multi-master Replication in a Cluster of Autonomous Databases*. In: Kosch H., Böszörményi L., Hellwagner H. (Eds) Euro-Par 2003 Parallel Processing, (Vol. 2790). Lecture Notes in Computer Science. Springer, Berlin, Heidelberg . https://doi.org/10.1007/978-3-540-45209-6_48

Thomson, A., Diamond, T., Weng, S.-C., & Ren, K. (2012). Calvin: Fast Distributed Transactions for Partitioned Database Systems. Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data. Scottsdale, Arizona, USA, May 20–24. https://doi.org/10.1145/2213836.2213838

Vucetic, M., Hudec, M., & Vujošević, M. (2013). A new method for computing fuzzy functional dependencies in relational database systems. Expert Systems with Applications, 40(7), 2738–2745. https://doi.org/10.1016/j.eswa.2012.11.019

Wedashwara, W., Mabu, S., Obayashi, M., & Kuremoto, T. (2006). Combination of genetic network programming and knapsack problem to support record clustering on distributed databases. Expert Systems with Applications, 46, 15–23. https://doi.org/10.1016/j.eswa.2015.10.006

Wiesmann, M., Pedone, F., Schiper, A., Kemme, B., & Alonso, G. (2000). Understanding replication in databases and distributed systems. Proceedings of the The 20th International Conference on Distributed Computing Systems (ICDCS 2000) (pp. 23–31), 464 p.




DOI: https://doi.org/10.15421/40270525

Refbacks

  • There are currently no refbacks.