28796 - Elements of Databases

Learning outcomes

At the end of the course the student: - knows the semi structured and unstructured data models in relationship with the central constructors of SQL/XML and XQuery; - knows the basic techniques of information retrieval and text analysis; - is capable of designing and developing a datawarehouse; - is capable of conducting a project using the basic data mining techniques

Course contents

• Structured, unstructured and semi structured data models.
• The limits of SQL
• An introduction to SQL/XML and XQuery
• The information retrieval models (boolean and vectors space) and web information retrieval (PageRank)
• The datawarehousing (OLAP) and data mining (Machine Learning)

Readings/Bibliography

• A. Moller et al - Introduzione a XML - Addison Wesley, 2007 ISBN: 9788871923734
• R. Baeza-Yates et al – Introduction to Information Retrieval - Cambridge University Press, 2008 ISBN: 0521865719
• P.N. Tan et al - Introduction to Data Mining - Financial Times Prentice Hall, 2019 ISBN: 9780273769224
• M. Golfarelli et al - Datawarehouse - Teoria e pratica della progettazione - McGrawHill, 2006 ISBN: 9788838662911

Teaching methods

The lectures will address the main issues related to the management and analysis of semi-structured and unstructured data.

Considering the type of activity and teaching methods adopted, the course attendance requires that all students follow Modules 1 and 2 of security training in study places, through e-learning.

Assessment methods

The examination consists of an oral assessment. Each academic year includes six examination sessions (“appelli”): two in the first session (January/February), three in the second (June/July), and one in the third (September). The dates are published on Almaesami and may vary. It is advisable to check 24 hours before the exam to check that the date has not changed.

To take part in an examination session, students must register via Almaesami at least 7 days before the exam date. Students who fail to register in time will not be allowed to sit the exam and must wait for the next session.

The assessment involves the discussion of a selected article and may include questions on topics covered during the course. Students are expected to demonstrate their ability to explore an advanced topic based on one of the proposed articles. The content of the article and the related topic must be discussed during the exam through a presentation prepared by the student.

During the oral examination, which lasts approximately 25/30 minutes, the student will critically present the chosen article using prepared slides. In addition, questions may be asked on any topic included in the syllabus. The oral examination is graded on a 30-point scale, taking into account the student’s preparation, presentation skills, and critical approach. The final mark is determined according to the following criteria:

• if preparation, presentation, and critical thinking are limited and emerge only with the lecturer’s guidance, the mark will range from 18 to 21;

• if preparation and presentation are satisfactory, but critical thinking and the ability to independently elaborate are only partially demonstrated, the mark will range from 22 to 25;

• if preparation, presentation, and critical thinking are good, and the student demonstrates adequate knowledge of the course topics, the mark will range from 26 to 29;

• if preparation, presentation, and critical thinking are excellent, with full command of the course content, the mark will be 30 or 30 with honours.

At the end of the oral examination, the student will be informed of the mark obtained and may choose whether to accept or decline it. In accordance with the University Teaching Regulations, the right to refuse a mark must be granted by the lecturer at least once for each course.

This course is integrated with the course 81940 - ARTIFICIAL INTELLIGENCE; consequently, the final grade can only be officially recorded within three days of obtaining both marks.

Useful links for the assessment:

• Instructor’s folder with papers to choose from: Google Drive link

•  Registration form of the project/article and further instructions for the submission: Link to the form

Teaching tools

Instructor's folder with slides and useful material: Google Drive link 

Links to further information

http://smartdata.cs.unibo.it/internships-thesis

Office hours

See the website of Danilo Montesi