Algorithms for massive datasets2025/26

Students:

• will be able to use technologies for the distributed storage of datasets;
• will know the MapReduce distributed processing framework and its leading extensions;
• will know the principal algorithms used in order to deal with classical big data problems, as well as to implement them using a distributed processing framework;
• will be able to choose appropriate methods for solving big data problems.

Lectures are based:

• on the textbook Mining of Massive Datasets, written by A. Rajaraman and J. Ullman (marked by RU in the calendar of lectures), available as a free download in the authors' Web site and published in hardcopy by Cambridge University Press (ISBN:9781107015357);
• on the notes and sample code published in the calendar of lectures.

The course explains the topics listed in the lecture calendar (available at the beginning of the course), covering the textbook contents as well as the contents of the remaining documents listed in Course material.

The course requires knowledge of the main topics of bachelor-level computer programming, calculus, probability, and statistics.

The exam consists of a project and an oral test, both related to the topics covered in the course. The project requires to process one or more datasets through the critical application of the techniques described during the classes, and is described in a written report.

The evaluation of the project, expressed with a pass/fail mark, considers the level of mastery of the topics and the clarity of the report. The oral test, which is accessed after a positive evaluation of the project, is based on the discussion of some topics covered in the course and on in-depth questions about the presented project. The evaluation of the oral test, expressed on a scale between 0 and 30, takes into account the level of mastery of the topics, clarity, and language skills.

Students should sign up at the chosen examination session through UniMia, and send en email to prof. Malchiodi within the project deadline (see table below), containing the link to the project. Students will be contacted after the project has been checked. The table below shows a tentative date for the oral exams.