77933 - Multimedia Data Management M

Academic Year 2023/2024

Learning outcomes

At the end of the course, the student has the knowledge and skills required for an 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. The student understands the architecture of traditional and advanced MM systems and services, search engines, social networks and recommendation systems.

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 is not required.


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

Teaching methods

Course lectures are in "traditional" classrooms and exploit the slides. Several use cases will be presented in order to show how such information technologies can be profitably applied in a number of real applications.

Provided slides are in english. Fluent spoken and written english is a necessary pre-requisite: all lectures and tutorials will be in english.

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 solution of the "free" exercise 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 lab programming 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 blackboard for the development of exercises.

Links to further information


Office hours

See the website of Ilaria Bartolini


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This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.