Dipartimento di InformaticaUniversità degli Studi di Milano
Dario Malchiodi

Big data analytics and technologies2015/16

PhD in Computer Science · University of Milan · Lectures are in English · 6 hours

Archived edition: this page is kept for reference, and some materials or links may no longer be available.

The course aims at illustrating some data analysis techniques characterized by scalability properties, as well as at implementing these techniques through exploitation of technologies expressly designed to be used in distributed environments. The course is co-held with prof. Santini.

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Course schedule

Lectures will take place at the Computer science department in the meeting room at 1st floor.

Office hours

By appointment, room 5015 of the Computer Science Department. It is possible contact the teacher by e-mail, taking care to read in advance the guide prepared by Prof. Sebastiano Vigna and clearly specifying in the message the course name and the academic year. In particular, students are encouraged to always use their academic address (i.e. based on the domain studenti.unimi.it) signing with name and student ID number and recalling that the response time may vary depending on the teacher commitments.

Course material

The course is based on the following textbook: Anand Rajaraman and Jeff Ullman, Mining of Massive Datasets, available both as a freely downloadable PDF and published in hardcopy by Cambridge University Press (ISBN:9781107015357). The suggested readings for the practical part are Holden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia, Learning Spark. Lightning-Fast Big Data Analysis, O'Reilly, 2015 (ISBN:978-1-449-35862-4) and Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills, Advanced Analytics with Spark. Patterns for Learning from Data at Scale, O'Reilly, 2015 (ISBN:978-1-491-91276-8)

Syllabus

The course explains the topics listed in the lecture calendar. The described software is published in two git repositories, respectively for theory and for lab parts.

Lectures calendar

Lecture Date Topic Material
L01 14/03/2016 14:30 - Big data and spark (Malchiodi)
    l01 15/03/2016 10:30 - Big data and spark (Santini)
      L02 15/03/2016 14:30 -Entity resolution (Malchiodi)
        l02 16/03/2016 10:30 - Entity resolution (Santini)
          L03 16/03/2016 14:30 -Machine learning (Malchiodi)
            l03 17/03/2016 10:30 - Machine learning (Santini)

              Exam modalities

              The exam consists in the discussion of a project to be agreed with teachers.