Subject

Modelling and Fusion of Unstructured Data

Course Title

Modelling and Fusion of Unstructured Data
Modeling and fusing unstructured data

Code

IS-Z-01

Study Programme

Intelligent systems

Organizer of the study programme (unit, institute, department or division)

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

Second cycle

Academic year / semester

9 / Winter

Number of ECTS credits

Teacher

Biljana Risteska Stojkoska, Kire Trivodaliiev, Slobodan Kajaldjiski

Prerequisites for enrolling in the course

—

10.

Objectives of the course programme (competences)

In this course, students will become familiar with data fusion from the perspectives of information, sensor, and multisensor fusion. They will become proficient in using data fusion methods, techniques, and algorithms. They will know how to apply various architectures and models for data fusion. The different types of data obtained through source fusion will need to be modeled as required. To this end, students will need to be trained in the modeling and representation of unstructured data, methods and strategies for information extraction from unstructured data, as well as techniques for representing the knowledge extracted from the data.

11.

Course content

1. Definition and fundamentals of data fusion, information fusion, and sensor/multisensor fusion. Classification based on the relationship between sources, the level of abstraction, and the input-output relationship.
2. Methods, techniques, and algorithms for data fusion. Decision-making techniques. Estimation techniques. Feature maps. Sensor abstractions. Compression. Information theory approach.
3. Architectures, models, and their characteristics. Information-based model. Activity-based models. Role-based models.
4. Paradigms for information fusion in the context of communication. Distributed paradigms. Information fusion and its divisions.
5. Modeling unstructured data. Review and comparison of existing data models and algorithms for efficient storage, retrieval, transmission, and display of data. Design of relevant abstract data types and their integration into existing modeling languages. Methods for quantifying the quality of data models.
6. Extracting relevant information from unstructured data. Lexicons and ontologies for representing common-sense knowledge. Integrating ontologies.

12.

Learning methods

Lectures supported by slide presentations, interactive lectures, exercises (using equipment and software packages), team work, case studies, guest lecturers, independent preparation and defense of a project assignment and a seminar paper, learning in an electronic environment (forums, 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 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.

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

22.2.

Additional literature

No. Author Title Publisher Year