MK EN
ФИНКИ

Subject

Knowledge-Based Information Systems

1. Course Title Knowledge-Based Information Systems
Knowledge-based information systems
2. Code m23_s_007
3. Study Programme —
4. Organizer of the study programme (unit, institute, department or division) Faculty of Computer Science and Engineering
5. Degree level (first, second, third cycle) Second cycle
6. Academic year / semester 10 / Summer
7. Number of ECTS credits 6
8. Teacher Kire Trivodaliiev, Slobodan Kajaldzhijski
9. Prerequisites for enrolling in the course —
10. Objectives of the course programme (competences) The student will be equipped to model and develop information systems based on
Knowledge through the use of modern knowledge discovery tools.
11. Course content Databases and knowledge bases. Modern tools for data analysis and retrieval (indexing and searching, distributed and parallel processing, web search engines, recommendation systems). Data warehouses and decision support systems. Analytical processing and data mining in data warehouses. Knowledge Discovery in Databases (KDD) technologies: selection, data cleansing (preprocessing, transformation), interpretation/evaluation. Knowledge Discovery in Big Data.
12. Learning methods Lectures supported by slide presentations, interactive lectures, practical exercises (using equipment and software packages), teamwork, case studies, invited guest lecturers, independent preparation and defence of a project assignment and seminar paper, and learning in an electronic environment (forums and consultations).
13. Total available time 6 ECTS x 30 hours = 180 hours
14. Distribution of available time 60 + 0 + 45 + 45 + 30 = 180 hours
15. Forms of teaching activities
15.1. Lectures - theoretical instruction 60 hours
15.2. Exercises (laboratory, auditory), seminars, teamwork 0 hours
16. Other forms of activities
16.1. Project assignments 45 hours
16.2. Independent assignments 45 hours
16.3. Home study 30 hours
17. Assessment method
17.1. Tests 15 points
17.2. Seminar paper / project (presentation: written and oral) 45 points
17.3. Activities and learning 15 points
17.4. Final exam 0 points
18. Grading criteria (points / grade)
up to 50 points5 (five) (F)
from 51 to 60 points6 (six) (E)
from 61 to 70 points7 (seven) (D)
from 71 to 80 points8 (eight) (C)
from 81 to 90 points9 (nine) (B)
from 91 to 100 points10 (ten) (A)
19. Requirement for obtaining a signature and taking the final exam completed activities 15.1 and 15.2
20. Language of instruction Macedonian and English
21. Method for monitoring the quality of teaching internal evaluation and survey mechanism
22. Literature
22.1. Required literature
1. E. Turban, J. E. Aronson, T-P. Liang, R. Sharda | Decision Support and Business Intelligence Systems | Prentice Hall | 2006
2. D.A. Grossman, O. Frieder | Information Retrieval (Algorithms and Heuristics) | Springer | 1998
3. S. Büttcher, C. L. A. Clarke, G. V. Cormack | Information Retrieval: Implementing and Evaluating Search Engines | MIT Press | 2016
4. Steven S. Skiena | The Data Science Design Manual | Springer | 2017
5. Charu C. Aggarwal | Recommender Systems: The Textbook | Springer | 2016
22.2. Additional literature
No. Author Title Publisher Year
Деканат Dean’s Office Студии Studies Акти Acts Проекти Projects