IA/0167 - CLOUD NETWORKING AND DATA ANALYSIS
Academic Year 2021/2022
Free text for the University
ALESSANDRO FLORIS (Tit.)
- Teaching style
- Lingua Insegnamento
|[70/91] INTERNET ENGINEERING||[91/00 - Ord. 2018] INGEGNERIA DELLE TECNOLOGIE PER INTERNET||5||50|
The objective of the course is to explain: the technological and architectural solutions for cloud networking and cloud computing; the main characteristics of Big Data and the solutions for Big Data analytics; the main methods for Big Data analysis, Big Data preprocessing and Big Data mining.
Knowledge and understanding
The objective of the course is to provide the students with knowledge and comprehension capacity regarding the technological and architectural solutions for cloud networking and cloud computing, the data analysis methods, the management of Big Data and the techniques for Big Data analytics for the cloud.
Applying Knowledge and understanding
The objective of the course is also to allow the students to apply their knowledge and comprehension capacity for: data analysis methods, data mining, data preprocessing, data classification and clustering methods; Big Data management and techniques for Big Data analytics for the cloud.
The expertise acquired by the students will allow them to evaluate the feasibility of the data analysis techniques to be used based on the data type and quantity to be processed, the applicability of the platforms and software for Big Data management and the implementation of Big Data analytics for the cloud.
The didactic approach and the methods of ascertaining the acquired knowledge will accustom the student to communicate the concepts and methods learned, as well as to formalize the problems in terms of data analysis methods, Big Data management, architectures and platforms and relevant configurations and to discuss the relevant solutions to specialist and non-specialist interlocutors.
Through the course, students will integrate the knowledge acquired in the other courses with reference to the Internet of Things, the protocols used in the Internet and the design of Smart City applications. Moreover, the carrying out of study activities and presentation of new topics in the class concerning the course topics will give students the ability to autonomously integrate the knowledge learned with the course with further topics and to summarize these topics in order to clearly set a presentation to the audience of colleagues.
The student must have adequate knowledge of Internet access technologies, Internet network protocols, Internet of Things, and basics of statistics. Furthermore, knowledge of technical English and relational data models and DBMS architectures is required. Not necessary, but advisable: Python programming bases.
The skills acquired from previous teachings concern the ability to analyze the basic architectures of telecommunication networks and to understand the logic of Internet of Things applications.
The skills acquired in previous preparatory teachings are essential for understanding, interpretation, critical analysis and resolution of network architectures and distributed systems.
Technologies and architectural solutions for Cloud networking and Cloud computing (8 theory)
Big Data management and Big Data analytics (10 theory and 7 exercises)
● Big Data Fundamentals
● Big Data storage
● Big Data processing
● Big Data analytics
Data analysis methods (15 theory and 10 exercises)
● Data types and statistics description
● Data preprocessing
● Data analysis e data mining
Teaching is organized in traditional ways with lectures with use of slides and classroom exercises using software. In addition, activities are organized for students to present additional topics assigned to them during the course.
Verification of learning
The student will be evaluated with a oral test and with the presentation of an experimental project aimed to verify the ability of the student to design and implement the analysis of a Big Data Dataset.
Gary Lee, Cloud Networking - Understanding Cloud-based Data Center Networks, Morgan Kaufmann Eds.
Jiawei Han, Micheline Kamber, Jian Pei, Data Mining - Concepts and Techniques - Third Edition, Morgan Kaufmann Eds.
During the course the slides and the traces of the exercises will be provided.