Data stream mining or Real-Time Analytics relies on and develops new incremental algorithms that process streams under strict resource limitations.
This course focuses on, as well as extends the methods implemented in open source tools as MOA and Apache SAMOA.
Students will learn to how select and apply an appropriate method for a given data stream problem; they will learn how to design and implement such algorithms; and they will learn how to evaluate and compare different solutions.
- Leçon : 12
- Travaux pratiques : 9
Format des notesNumérique sur 20Littérale/grade européen
Pour les étudiants du diplôme Data & Knowledge (D-K)
Vos modalités d'acquisition :
First session: 2/3 Exam +1/3 Labs (E)
Second session: 100% Exam
- Crédits ECTS acquis : 2.5 ECTS
La note obtenue rentre dans le calcul de votre GPA.
This module will present concepts, architectures and algorithms for IoT big data processing and analytics, at a very large scale, in distributed settings.
The following topics will be covered:
● Apache Spark
● Apache Flink
● Apache Beam/Google Cloud DataFlow
● Apache Storm
● Lambda and Kappa Architectures
A strong focus will be given to labs in this class, so that students can gather enough experience with different existing systems, and understand their respective advantages. The architecture of all distributed computing systems will be discussed in detail during lectures.