Syllabus
1. Information regarding the programme
1.1 Higher education institution 	Babes-Bolyai University	
1.2 Faculty	Faculty of Mathematics and Computer Science	
1.3 Department	Computer Science	
1.4 Field of study	Computer Science	
1.5 Study cycle	Master	
1.6 Study programme / Qualification 	High Performance Computing and Big Data Analytics	

2. Information regarding the discipline 
2.1 Name of the discipline	Knowledge Discovery in Wide Area Networks							
2.2 Course coordinator 	Lect. Christian Sacarea, PhD							
2.3 Seminar coordinator	Lect. Christian Sacarea, PhD							
2.4. Year of study	2	2.5 Semester	2	2.6. Type of evaluation	E	2.7 Type of discipline	compulsory	

3. Total estimated time (hours/semester of didactic activities) 
3.1 Hours per week 	4	Of which: 3.2 course	2	3.3 seminar/laboratory	1	
3.4 Total hours in the curriculum 	42	Of which: 3.5 course	28	3.6 seminar/laboratory	14	
Time allotment:	hours					
Learning using manual, course support, bibliography, course notes	30					
Additional documentation (in libraries, on electronic platforms, field documentation) 	30					
Preparation for seminars/labs, homework, papers, portfolios and essays	30					
Tutorship	20					
Evaluations	23					
Other activities: ..................						
3.7 Total individual study hours 	133					
3.8 Total hours per semester	175					
3.9 Number of ECTS credits	7					

4. Prerequisites (if necessary)
4.1. curriculum	•		
4.2. competencies	•	Good skills in understanding, analyzing, modelling real life problems
•	Programming skills
•	Social and communication skills	

5. Conditions (if necessary)
5.1. for the course	•	•		
5.2.  for the seminar /lab activities	•	•		

6. Specific competencies acquired 
Professional competencies	•	to offer the main conceptual and computational tools of Artificial Intelligence
develop skills for coping with real world problems
develop research abilities  	
Transversal competencies	•	Ability to analyze a large amount of information
Ability to communicate with non-experts and to find altogether solutions for real-life problems	
7. Objectives of the discipline (outcome of the acquired competencies)
7.1 General objective of the discipline
	•	To introduce the student to a broad range of information representation models drawn from the fields of information science, computer science, semiotics, philosophy, cognitive psychology, and artificial intelligence.
•	To introduce a formal method of qualitative data analysis. 
•	To provide practical experience with basic data analysis techniques, such as selection, grouping and scaling of features. 
	
7.2 Specific objective of the discipline
	•	To develop the student's ability to understand the problems involved in the formalization of informal data. 
•	To teach practical skills of using the computer software DIAGRAM, ANACONDA, and TOSCANA. 
•	To provide practical experience with techniques of structuring graphical representations.
•	To provide insights into the formal structure of classification systems.	

8. Content
8.1 Course	Teaching methods	Remarks	
1.	Introduction: Data, Information, and Knowledge. What is data? Information? Knowledge? How are they represented? Information access and information usage, The interdisciplinarity of information science.	Lectures, presentations, conversations		
2.	Introduction in Knowledge Management. Acquisition, representation and computing of knowledge. Knowledge bases for constructions. 	Lectures, presentations, conversations		
3.	Formal Concept Analysis (FCA). The pragmatic approach.	Lectures, presentations, conversations		
4.	FCA. Context, concept, diagrams	Lectures, presentations, conversations		
5.	Order relations	Lectures, presentations, conversations		
6.	Many-valued contexts. Scaling	Lectures, presentations, conversations		
7.	Conceptual hierarchies. Diagram. How to draw a nice diagram ?	Lectures, presentations, conversations		
8.	Implications.	Lectures, presentations, conversations		
9.	Association rules.	Lectures, presentations, conversations		
10.	Conceptual Knowledge Processing.	Lectures, presentations, conversations		
11.	Factor analysis	Lectures, presentations, conversations		
12.	Ordinal factor analysis	Lectures, presentations, conversations		
13.	Knowledge Management Systems 	Lectures, presentations, conversations		
14.	Conceptual Knowledge Acquisition	Lectures, presentations, conversations		
Bibliography

1.	Bernhard Ganter, Rudolf Wille, Formal Concept Analysis, Springer Verlag, 2000
2.	Aldo de Moord, Wilfried Lex, Bernhard Ganter, eds., Conceptual Structures for Knowledge Creation and Communication, Springer LNAI 2746, 2003.
3.	Bernhard Ganter, Aldo de Moord, eds., Using Conceptual Structures, Shaker Verlag, 2003.
4.	Frank Vogt, Formal Concept Analysis with C++, Springer, 1996
Rokia Missaoui, Jürg Schmid, eds., Formal Concept Analysis, Springer LNAI 3874, 2006.		
8.2 Seminar / laboratory	Teaching methods	Remarks	
1.	Working with small data sets	projects, exercises, individual study, 
homework assignments.		
2.	Diagram drawing. What is a nice diagram?	projects, exercises, individual study, h
omework assignments.		
3.	ConExp, Toscana Suite	projects, exercises, individual study, 
homework assignments.		
4.	Nested line diagrams	projects, exercises, individual study, 
homework assignments.		
5.	My first knowledge management system	projects, exercises, individual study, 
homework assignments.		
6.	Mining associations	projects, exercises, individual study,
 homework assignments.		
7.	Attribute exploration	projects, exercises, individual study, 
homework assignments.		
Bibliography

1.	B. Ganter, G. Stumme, R. Wille, eds.  Formal Concept Analysis: foundations and applications, Springer LNAI 3626, 2005
2.   P. Becker, J. Hereth Correia:  The ToscanaJ Suite for implementing conceptual information systems, in 1, pp. 324 - 348
3.   C. Carpineto, G. Romano, Concept data analysis: theory and applications, Wiley, 2004.
4.   C. Carpineto, G. Romano,  Using concept lattices for text retrieval and mining, in 1, pp. 161- 179		

9. Corroborating the content of the discipline with the expectations of the epistemic community, professional associations and representative employers within the field of the program

•	•	 Knowledge discovery is an interdisciplinary area which is broadly needed by all actors from science, economy, industry or research.	

10. Evaluation
Type of activity	10.1 Evaluation criteria	10.2 Evaluation methods	10.3 Share in the grade (%)	
10.4 Course	Fundamental principles	Project	60%	
	Applying the methods for problem solving			
10.5 Seminar/lab activities	Implementing concepts and algorithms		40%	
	Innovation, initiative, team work			
10.6 Minimum performance standards				
•	At least grade 5 (from 1 to 10).				

Date			Signature of course coordinator 	Signature of seminar coordinator
..........................		Lect. Christian Sacarea, PhD	Lect. Christian Sacarea, PhD

Date of approval  					  Signature of the head of department	
...........................................					Univ. Prof. Bazil Parv, PhD