First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
COMPUTER ENGINEERING
Course unit
BIG DATA COMPUTING
INP7079233, A.A. 2017/18

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course Second cycle degree in
COMPUTER ENGINEERING
IN0521, Degree course structure A.Y. 2009/10, A.Y. 2017/18
N0
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination BIG DATA COMPUTING
Department of reference Department of Information Engineering
Mandatory attendance No
Language of instruction English
Branch PADOVA

Lecturers
Teacher in charge ANDREA ALBERTO PIETRACAPRINA INF/01

Mutuated
Course unit code Course unit name Teacher in charge Degree course code
INP7079233 BIG DATA COMPUTING ANDREA ALBERTO PIETRACAPRINA IN2371
SCP7079297 BIG DATA COMPUTING ANDREA ALBERTO PIETRACAPRINA SC2377
INP7079233 BIG DATA COMPUTING ANDREA ALBERTO PIETRACAPRINA IN2371

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines INF/01 Computer Science 2.0
Core courses ING-INF/05 Data Processing Systems 4.0

Mode of delivery (when and how)
Period Second semester
Year 1st Year
Teaching method frontal

Organisation of didactics
Type of hours Credits Hours of
teaching
Hours of
Individual study
Shifts
Lecture 6.0 48 102.0 No turn

Calendar
Start of activities 26/02/2018
End of activities 01/06/2018

Syllabus
Prerequisites: Competences regarding the desing and analysis of algorithms and data structures, and knowledge of fundamental notions of probability and statistics.
Target skills and knowledge: The course will provide the student knowledge of the main tools and methodologies used in the analysis of, possibly large, datasets.
Examination methods: Compulsory written exam and (group) project. Projects are presented and discussed at the end of the course or, at the student's discretion, after passing the written exam.
Assessment criteria: The final score is obtained by combining the scores of the written exam and of the project.
Course unit contents: The course will cover the following topics:

Introduction to the Big Data phenomenon
Programming frameworks: MapReduce/Hadoop, Spark
Association Analysis
Clustering
Graph Analytics (metriche di centralità, scale-free/Power-law graphs, fenomeno dello small world, uncertain graphs)
Similarity and diversity search
Planned learning activities and teaching methods: Lectures and activities related to the execution of the project
Additional notes about suggested reading: The lectures' diary, course material, and detailed exam rules will be made available on the course web site accessible from MOODLE.
Textbooks (and optional supplementary readings)
  • J. Leskovec, A. Rajaraman and J. Ullman, Mining Massive Datasets. --: Cambridge University Press, 2014. Available in pdf Cerca nel catalogo