91269 - Multimedia Data Management

Learning outcomes

The course aims to provide the knowledge and skills necessary for the effective and efficient management of multimedia (MM) data, with particular attention to the problems of MM data representation, MM data retrieval models, and interaction paradigms between the user and the MM system (both for purposes of data presentation and exploration). We first consider architectures of traditional ("standalone") MM systems; then, we concentrate on more complex MM services, by primarily focusing on search engines, social networks and recommendation systems.

Course contents

The course intends to provide students with methodology, modeling, design, and implementation skills/expertise related to the development of smart "MM data-intensive" applications and services like the the ones included in commercial solutions like Google search engine, YouTube, Facebook, Instagram, Flickr, and Twitter, just to named a few, but especially possible smarter extensions.

Constraints on previous knowledge: nothing.
However, the contents of the courses like "Design of Web Applications T", " Software Engineering T " and, only very partially, "Database and Big Data Technologies M" could be useful and valuable in a few parts of the course.

CONTENTS:

Basics on Multimedia Data Management

Multimedia data and content representations

• MM data and applications
• MM data coding
• MM data content representation

How to find MM data of interest

• Description models for complex MM objects
• Similarity measures for MM data content
• MM Data Base Management Systems

Efficient algorithms for MM data retrieval

• MM query formulation paradigms
• Sequential retrieval of MM data
• Index-based retrieval of MM data

Automatic techniques for MM data semantic annotations

Browsing MM data collections
MM data presentation

• User interfaces
• Visualization paradigms
• Dimensionality reduction techniques

Result accuracy, use cases and real applications

• Quality of the results and relevance feedback techniques
• Use cases and demos of some applications

Multimedia Data on the Web

• Web search engines
• Graph-based data: semantic Web and social networks
• Web recommender systems

 

N.B. For students of the second cycle degree programmes (LM) in Artificial intelligence and Computer Science, the "Efficient algorithms for MM data retrieval" part of the program (that counts the 2 extra CFU with respect to the 8 CFU of the master course in Computer Engineering) is not required for the final exam.

N.B. Students can decide to take the final exam in English or Italian. 

Readings/Bibliography

Education material provided by the teachers (copies of the slides used in the classroom, scientific literature, bibliography, and useful links).

Teaching methods

Course lectures are in "traditional" classrooms and exploit the slides. During the lectures, the general issues related to all topics of the program will be discussed thoroughly.

Several real use cases will be presented and discussed in order to show how MM data management techniques can be profitably applied in a number of smart MM applications and services.

The course will be accompanied by a practical project, where students will be solicited to complete guided activities, including some amount of autonomous personal work. This project will be needed to complete the exam preparation and to achieve the desired abilities/skills.

Assessment methods

Achievements will be assessed by the means of a final exam. This is based on an analytical assessment of the "expected learning outcomes" described above. In order to properly assess such achievement the examination is composed of an oral exam.

The admission of the students to the final examination is constrained to the upload of the complete practical project into the dedicated OneDrive folder "MDM", following the instructions provided by the teacher (see the slides of the course presentation for more details).

To participate to the final exam, interested students have to register themselves by exploiting the usual UniBO Web application, called AlmaEsami.

Higher grades will be awarded to students who demonstrate an organic understanding of the subject, a high ability for critical application, and a clear and concise presentation of the contents. To obtain a passing grade, students are required to at least demonstrate a knowledge of the key concepts of the subject, some ability for critical application, and a comprehensible use of technical language. A failing grade will be awarded if the student shows knowledge gaps in key-concepts of the subject, inappropriate use of language, and/or logic failures in the analysis of the subject.

Teaching tools

Classroom lessons will be held using slides, which will be integrated with the use of the PC laptops and blackboard for the development of the practical project.

Links to further information

http://www-db.disi.unibo.it/courses/MDM/

Office hours

See the website of Ilaria Bartolini