Intelligent data processing in distributed information environments

Course: Computer science

Structural unit: Faculty of information Technology

Title
Intelligent data processing in distributed information environments
Code
ВК 2.9
Module type
Вибіркова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2022/2023
Semester/trimester when the component is delivered
8 Semester
Number of ECTS credits allocated
4
Learning outcomes
Use tools for the development of client-server applications, design conceptual, logical and physical models of databases, develop and optimize queries to them, create distributed databases, data stores and showcases, knowledge bases, including on cloud services, using web languages -programming; Perform parallel and distributed calculations, apply numerical methods and algorithms for parallel structures, parallel programming languages in the development and operation of parallel and distributed software; Use knowledge and practical skills in architecture and development technology of modern intelligent software systems; Evaluate, select and use intelligent technologies when solving practical problems of processing structured and unstructured information in distributed information and software environments.
Form of study
Distance form
Prerequisites and co-requisites
Missing
Course content
As part of the discipline "Intelligent data processing in distributed information environments", the basics of building distributed information environments, distributed data processing technologies, distributed data storage systems, distributed data storage systems based on blockchain technology, data reconciliation policies, design technologies and distribution of developed systems are considered. intelligent data processing in distributed information environments, systems of distributed artificial intelligence, intelligent multi-criteria evaluation in distributed environments, issues of collective expert evaluation in distributed environments. The goal of the discipline is to provide students with theoretical knowledge and practical skills of intelligent data processing in distributed information environments using modern scientific research and software, design and implementation of distributed intelligent systems.
Recommended or required reading and other learning resources/tools
Planned learning activities and teaching methods
Lectures, laboratory work, independent work
Assessment methods and criteria
The level of achievement of all planned learning outcomes is determined by the results of written test papers, performance of laboratory and independent works. Assessment of students is carried out during the semester for all types of work. The total score for the semester is formed as a weighted sum of points earned by the student for various types of work. The maximum number of points that a student can receive for work in a semester does not exceed 100 points. The form of assessment is an exam. The maximum number of points that can be obtained by a student is 40 points on a 100-point scale. If a student gets less than 24 points during the exam, he is considered "unsatisfactory" and the points scored are not counted. A student is not allowed to take the exam if he scored less than 36 points during the semester (less than 60% of the maximum possible number of points that the student can receive for work in the semester).
Language of instruction
Ukrainian

Lecturers

This discipline is taught by the following teachers

Departments

The following departments are involved in teaching the above discipline