BME VIK - Médiatartalom- kezelő rendszerek

A tantárgy neve magyarul / Name of the subject in Hungarian: Médiatartalom- kezelő rendszerek

3. Course coordinator and department Dr. Magyar Gábor Béla,

4. Instructors

MAGYAR, Gabor Ph.D., Department of Telecommunications and media-informatics

SZŰCS, Gabor PhD., Department of Telecommunications and media-informatics

6. Pre-requisites

Kötelező:

NEM ( TárgyEredmény( "BMEVITMMB01" , "jegy" , _ ) >= 2
VAGY
TárgyEredmény("BMEVITMMB01", "FELVETEL", AktualisFelev()) > 0)

A fenti forma a Neptun sajátja, ezen technikai okokból nem változtattunk.

A kötelező előtanulmányi rend az adott szak honlapján és képzési programjában található.

7. Objectives, learning outcomes and obtained knowledge The developed syllabus is concerned with introducing the students to the identification, assessment and analysis of the Media Content Management Systems (MCMS) and it contains more parts based on content handling, search and display. Information retrieval (IR) is a large part in the course outline, containing basic measures, search techniques, indexing, ranking procedures, Boolean, vector and probabilistic models, weighting schemes. The syllabus presents different CMS types. For an efficient handling of the contents the metadata schemes and standards are needed. Semantic content management is also an important part, as a general method for conceptual description or modeling of information.

8. Synopsis

1. Fundamentals. Data, information,, content. Structured, semistructured, unstructured data, characterictics. Modelling, data schemes.

2. Content Management. CMS model, CMS types. Digital Asset Management, WCMS, Knowledge Management, Enterprise Content Management. CMS plan, design, integration. CMS subsystems.

3. Content management standards. OASIS. DITA: concepts, topics, DITA-map, products. Re-usability, content, design, processes.

4. Semantic web model, layers. URI, XML, XML scheme, XML name space, RDF, RDF scheme, semantic graphs.

5. Semantic web layers, thesauri, ontology, logics, proof, trust.

6. Information Retrieval. Fundamentals of Information Retrieval (IR). General information retrieval model. IR architecture

7. Boolean, Vector, Probabilistic models. Logical views.

8. Index, taxonomy, folksonomy. PageRank, HITS algorithms. Precision and recall. Ranking.

9. Web Search Engine. General Web Search Engine Architecture. Indexer, storage, crawler. Web search engine generations. Meta search.

10. Multimedia databases. Linked and embedded content methods. Multimedia retrieval. Metadata-based retrieval. Representation of multimedia data.

11. Audiovisual archives. Conceptions, architecture, functionality.

12. e-Learning basics, standards (SCORM, LOM, QTI). Systems functionality, architecture.

13. Knowledge management. Knowledge categories, creation, conversion. Knowledge management approaches. Knowledge map. Competence types and grades. Human and embedded knowledge. Knowledge transfer, organizations, tasks and topics.

14. Case studies.

9. Method of instruction Lectures and practices.

10. Assessment

a. There is 1 in-class test (ZH) in the semester.

b. In the examination period: written examination with the possibility of oral one, there is no preliminary examination date. Optional course-work can be part of evaluation.

c. Condition for the signature is the pass mark of ZH test (40% above).

11. Recaps There is one chance to repeat the test in the teaching period.

12. Consultations Preliminary appointing with instructors or after the lectures

13. References, textbooks and resources R. Baeza-Yates, , B. Ribeiro-Neto: Modern Information Retrieval. Addison-Wesley

14. Required learning hours and assignment

Kontakt óra	42
Félévközi készülés órákra	18
Felkészülés zárthelyire	12
Házi feladat elkészítése
Kijelölt írásos tananyag elsajátítása
Vizsgafelkészülés	48
Összesen	120

15. Syllabus prepared by MAGYAR, Gabor Ph.D., Department of Telecommunications and media-informatics

SZŰCS, Gabor PhD., Department of Telecommunications and media-informatics