Data Analysis and Query Languages
News
[01.09.2016, 13:30] The inspection of the exam will take place on 08.09.2016 between 16:30 and 17:30 in room 051-01-029.
[01.09.2016, 13:30] Grades have been uploaded to HisInOne.
[12.08.2016, 10:00] ATTENTION: the time of the examination on 15.08. is 09.00 and not 10.00 as wrongly reported in some exercise sheets. The time was corrected in the website.
Lecturer
Assistants
Anas Alzoghbi and Victor Anthony Arrascue Ayala
Tutors
Oleg Zharkov
zharkovo@informatik.uni-freiburg.de
Visar Boshnjaku
visanbo@informatik.uni-freiburg.de
Course Contents
The lectures cover a variety of topics on data analysis and query languages, mostly focussing on web data. As the amount of available data is still increasing, recommendation is discussed as an alternative querying paradigm. Some relevant machine learning techniques useful for finding recommendations are introduced as well.- Queries versus Recommendations
- RDF-graphs and SPARQL
- Link Analysis: How to Rank Pages
- Similarity of Documents
- Similar Interests: User- and Item-based Collaborative Filtering
- Similar Interests: Content-based Filtering
- Recommender Systems: Towards the Full Spectrum of Technologies
- Graph Analysis
- Hybrid Recommender Systems and other Ideas
Material
Slides, exercise sheets and other material can be found in ILIAS.
Necessary Prerequisites
The key course (Kursvorlesung) 'Databases and Information Systems' or an equivalent Database course.
Time, Location and Organization:
Lecture: Tuesday 14 - 16 and Friday 10 - 12 (in case there is no tutorial scheduled)
Tutorials: Friday 10 - 12 (in case there is no lecture scheduled)
Location: G.-Köhler-Allee 101, Seminar 01-009/013
Language: English
ECTS: 6 Points
Program of Study: Bachelor CS, Master CS/ESE, Lehramt Informatik
Exam:
Exam modality: written
Time: 15.08.2016, 09.00 a.m. (s.t)
Location: Hörsaal 036 in Gebäude 101
Exercises
- Tutorials will take place on Fridays as announced (the first session will take place on 29.04.2015).
- Exercises will consist of theoretical and pratical tasks.
- Pratical tasks require some programming proficiency but this will be very helpful to directly apply the learnt knowledge.
- 50% of the overall points as a prerequisite for the exam.
Literature and Additional Material
- Anand Rajaraman and Jeffrey David Ullman. 2011. Mining of Massive Datasets. Cambridge University Press, New York, NY, USA. Publisher. Download (see terms of use).
- Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, May 2014. ISBN: 9780521766333. Publisher.
- Recommender Systems Handbook. Editors: Francesco Ricci, Lior Rokach, Bracha Shapira, Paul B. Kantor. Springer Verlag, 2011.