logo-polimi
Loading...
Degree programme
Programme Structure
Show/Search Programme
Course Details
Save Document
Degree Programme
Read Degree Programme
Faculty
Infrastructures
Quantitative data
International context
Customized Schedule
Your customized time schedule has been disabled
Enable
Search
Search a Professor
Search a Course
Search a Course (system prior D.M. n. 509)
Search Lessons taught in English

Glossary
Semester (Sem)
1First Semester
2Second Semester
AAnnual course
Educational activities
BIdentifying activities
Language
Course completely offered in italian
Course completely offered in english
--Not available
Innovative teaching
The credits shown next to this symbol indicate the part of the course CFUs provided with Innovative teaching.
These CFUs include:
  • Subject taught jointly with companies or organizations
  • Blended Learning & Flipped Classroom
  • Massive Open Online Courses (MOOC)
  • Soft Skills
Course Details
Context
Academic Year 2019/2020
School School of Industrial and Information Engineering
Name (Master of Science degree)(ord. 270) - MI (481) Computer Science and Engineering
Track T2A - COMPUTER SCIENCE AND ENGINEERING
Programme Year 2

Course Details
ID Code 054306
Course Title UNSTRUCTURED AND STREAMING DATA ENGINEERING
Course Type Mono-Disciplinary Course
Credits (CFU / ECTS) 5.0
Semester First Semester
Course Description The course provides the foundational concepts and methods for designing, storing, analyzing and managing semi-structured and unstructured data, both in batch and in streaming. The course aims to tame the variety (data in many forms) and velocity (analyzing data streams to enable real-time decisions) dimensions of Big Data, without forgetting the volume dimension. The variety-oriented part of the course will focus on NoSQL (and not-only-SQL) models and technologies. Students will learn how to select appropriate data management solutions to deal with scalability, availability, consistency, performance and expressiveness requirements. The velocity-oriented part of the course will focus on time series, data streams and events both from a deductive and an inductive perspective. The deductive one focuses on domain-specific languages and knowledge representation techniques. The inductive one examines machine-learning problems focusing on massive online learning and, in particular, on the ability to learn when to forget past information. Finally, the course will cover the basic aspects of the data analysis pipeline: acquisition, integration, exploration, mining, analytics, visualization, and interpretation. Students will perform hands-on activities on cloud-based Big Data platforms oriented to variety and velocity, addressing real-life data management and data science scenarios.
Scientific-Disciplinary Sector (SSD)
Educational activities SSD Code SSD Description CFU
B
ING-INF/05
INFORMATION PROCESSING SYSTEMS
5.0

Schedule, add and removeAlphabetical groupProfessorLanguageCourse details
From (included)To (excluded)
--AZZZZDella Valle Emanuele
manifesti v. 3.1.7 / 3.1.7
Area Servizi ICT
14/11/2019