Modeling and fusing unstructured data

1.

Subject title

Modeling and fusing unstructured data

Моделирање и фузирање на неструктурирани податоци

2.

Code

IS-Z-01

3.

Study program

Inteligent Systems, Cloud Computing, Data science in computer science and engineering, Bioinformatics, Security, Cryptography and Coding, IT management, Еducation with ICT, Eco-informatics, Internet Technologies and cyber security, Computer Science, Software for embedded systems, Software Engineering, Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Software Engineering, Statistics and Data Analytics, Statistics and Data Analytics,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Втор циклус

6.

Academic year / semester

5 / Зимски

7. Number of ECTS credits

6.0

8.

Instructor

проф. д-р Билјана Ристеска Стојкоска проф. д-р Кире Триводалиев проф. д-р Слободан Калајџиски

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

In this course students will be introduced to the data fusion in terms of information, sensor and multisensory fusion. They will be able to use the methods, techniques and algorithms for data fusion. They will know how to apply different architectures and patterns for data fusion. The different types of data that would be obtained through fusion of sources will need to be modeled according to needs. To this end, students will need to be capable of modeling and representing unstructured data, methods and strategies for extracting information from unstructured data, as well as the techniques for presenting the knowledge extracted from the data.

11.

Subject content:

1. Definition and bases of data fusion, information fusion, sensor/multisensory fusion. Classification according to the relation of sources, according to the level of abstraction and according to the relation-exit. 2. Methods, Techs and Data Fusion algorithms. Decision -making techniques. Assessment techniques. Maps of features. Sensor abstractions. Compression. Access from information theory. 3. Architectures, models and their features. Information based model. Models based on activities. Role -based models. 4. Information fusion paradigms in the context of communication. Distributed paradigms. Information fusion and its divisions. 5. Modeling unstructured data. Overview and comparison of existing data models and algorithms for efficient storage, search, transmission and display of data. Design of relevant abstract types of data and their integration into existing modeling languages. Methods for quantifying the quality of data models. 6. Extracting relevant information from unstructured data. Lexicones and ontologies for presenting mallerazian knowledge. Integrating ontologies.

12.

Learning methods:

Предавања поддржани со презентации преку слајдови, интерактивни предавања, вежби (користење на опрема и софтверски пакети), тимска работа, пример случаи, поканети гости предавачи, самостојна изработка и одбрана на проектна задача и семинарска работа, учење во електронско опкружување (форуми, консултации).

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

60 + 0 + 45 + 45 + 30 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

60 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

0 hours

16.

Other forms of activities

16.1.

Project tasks

45 hours

16.2.

Independent tasks

45 hours

16.3.

Homework

30 hours

17.

Grading method

17.1.

Tests

15 points

17.2.

Seminar work / 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 points

5 (five) (F)

from 51 to 60 points

6 (six) (E)

from 61 to 70 points

7 (seven) (D)

from 71 to 80 points

8 (eight) (C)

from 81 to 90 points

9 (nine) (B)

from 91 to 100 points

10 (ten) (A)

19.

Condition for signature and taking final exam

реализирани активности 15.1 и 15.2

20.

Language of instruction

македонски и англиски

21.

Quality assurance method

механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature