Data Storage and Processing Systems Engineering

Major: Software Engineering
Code of subject: 7.121.01.E.021
Credits: 5.00
Department: Software
Lecturer: professor Melnyk Roman Andriyovych
Semester: 2 семестр
Mode of study: денна
Learning outcomes: РНС1.1. Be able to apply technologies of data processing, storage and transferring between different modern information infrastructure mediums including those for data format recognition and real-time report generation. РНС1.2. Know data transformation algorithms for object extraction, analysis and classification and be able to use cloud technologies for analytical calculations and data storage.
Required prior and related subjects: Prerequisites: Image Processing Using Artificial Intelligence Methods Corequisites: Master’s Thesis Preparation and Defence
Summary of the subject: Relational data models and DBMS implementing them. Requests for different types of data sources. Inverted indices. Mathematical apparatus of description. Operations on data and their implementation. Data analysis Data visualization. Non-relational data models. Associations between records. Examples of non-relational databases. MongoDB as a common database. Queries to non-relational databases. Searching for sources by metadata. Keyword search operations. Data processing in Mongo DB. Different data sources, queries, keywords, indexes. Integration of data from different sources. ETL technology. EII technology. EAI technology. ECM technology. WEB data integration. Integration of information systems on based on Web services. Protocols of services. Architecture of OLAP systems. Processing of data from various sources in modern technologies and in various programming languages. Data space model. Multi-level structures. Examples of data management systems based on MySql for direct and program use. Procedural and object approach. Programming tools in DBMS. Accelerated data search algorithms. Cluster analysis. An example of data storage in MS SQLServer. Query language, procedures. Network transmission mechanisms. Distributed data storage. Problems, advantages, disadvantages on the examples of VISA, Mastercard, etc. Data storage networks based on GRID examples, possible architecture. Data storage in the clouds using the Azure system as an example. Software interface. Comparison with other technologies. Data storage in the clouds using the example of the AWS system by Amazon. Access mechanisms. Data dimensionality. An example of data storage in the VNS system. Data types, entities, attributes. System performance.
Assessment methods and criteria: Current control (45%): written reports on laboratory work (30%, 3 labs 10% each), tests on classes (15%) Final control (55%, exam), testing (50%), oral examination - (5%)
Recommended books: 1. Дейт К.Дж. Введение в системы баз данных, 8-е издание.: Пер. с англ. . – М.: Изд. Дом «Вильямс», 2017. – 1328 с. 2. Коннолли Т., Бегг К. Базы данных. Проектирование, реализация и сопровождение. Теория и практика, 3-е изд. – М.: Изд. Дом «Вильямс», 2017. – 1440 с. 3. Ramez Elmasri, Shamkant B. Navathe. FUNDAMENTALS OF Database Systems SEVENTH EDITION [Електронний ресурс] – Режим доступу: https://www.auhd.site/upfiles/elibrary/Azal2020-01-22-12-28-11-76901.pdf 4. Distributed Database Management Systems [Електронний ресурс] – Режим доступу:https://cs.uwaterloo.ca/~tozsu/courses/cs856/F02/lecture-1-ho.pdf 5. Distributed DBMS Tutorial [Електронний ресурс] – Режим доступу: https://www.tutorialspoint.com/distributed_dbms/index.htm