Ing Ind - Inf (Mag.)(ord. 270) - MI (474) TELECOMMUNICATION ENGINEERING - INGEGNERIA DELLE TELECOMUNICAZIONI
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056895 - STREAMING DATA ANALYTICS
Ing Ind - Inf (Mag.)(ord. 270) - MI (481) COMPUTER SCIENCE AND ENGINEERING - INGEGNERIA INFORMATICA
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056895 - STREAMING DATA ANALYTICS
Ing Ind - Inf (Mag.)(ord. 270) - MI (487) MATHEMATICAL ENGINEERING - INGEGNERIA MATEMATICA
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ZZZZ
056895 - STREAMING DATA ANALYTICS
Obiettivi dell'insegnamento
The course provides the foundational concepts, methods, languages, and systems for ingesting, processing, and analyzing data that flows to enable real-time decisions. The course aims to the tame velocity dimensions of Big Data without forgetting the volume and variety dimensions.
Risultati di apprendimento attesi
Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will learn how to:
Identify problems that can be addressed with big data techniques tailored for velocity
apply the stream data analysis technologies for solving real-world problems
Applying knowledge and understanding
Given specific project cases, students will be able to:
Define and implement a streaming data analysis solution for the problem
Apply it on real data streams from social media and IoT sensors
Making judgements
Given specific project cases, students will be able to:
Learn how to decide which streaming data analysis solution to apply and how to evaluate this decision
Communication
Students will learn to:
Write a report on a project describing and motivating the decisions taken and the results obtained
Present their work in front of their colleagues and teachers
Lifelong learning skills
Students will learn how to develop a realistic streaming data analysis project in all its phases
Argomenti trattati
Foundations of streaming algorithms
when random access is forbidden and a polylog complexity is acceptable
the turnstile and cash register models
the sliding window model
Streaming Data Engineering
from data streams and time-series to complex event recognition and processing
event-driven architecture and its role in modern Data Analytics practically illustrated using Kafka
Students are expected to know the basics about: database management and SQL
Modalità di valutazione
The exam consist of a theoretical part (written exam) and an optional practical part (project work with oral presentation)
The written exam is composed of a mix of theoretical questions regarding any course subjects and exercises regarding the technical content and how to apply it in practice. Students can get up to 30L in the written test.
The optional practical project requires to use of one or more of the technologies presented in the lectures. It consists in solving a realistic streaming data analysis problem based on real or realistic datasets publicly available or provided by the teachers. Only students, who will get at least 26/30 in the written exam, can opt for it.
The final grade is computed as follows: written text result + optional practical project result. E.g., written text 26 + optionalpractical project 5 = 30L
Type of assessment
Description
Dublin descriptor
Written test
Theoretical questions
Exercises focusing on streaming data analysis aspects
1,4
1, 2, 3
Assessment of project artefacts
Assessment of the design and implmenetation of the practical peoject work developed by the student
2, 3, 5
Oral presentation
Assessment of the presentation of the practical peoject developed by the student
Inglese