====== Technologies for Big Data Management ====== ---- ===== News ===== * **Projects:** the list of the **projects** is available at following {{https://drive.google.com/drive/folders/1gFg_sGXI-29ClcwHuqW9BfHOXKHeEYhG | link}}. * **No lesson on 14 November:** no lesson on this day. * **TBDM on Telegram**: students can join to the Telegram group with the following {{https://t.me/+_If79QPfFR42NzQ0 | link}}. * **No lesson on 11 November:** no lesson on this day. * **No lesson on 31 October:** no lesson on this day. * **Webex recordings**: recordings are available at the following [[https://docs.google.com/document/d/1JYz2CP5fzmcMjYVzYw8S4NNfHTirVYdF4AwWyJ9C14g/view | link]]. * **Course start**: the course is planned to start the **30th of September** - AB2 - Polo Lodovici A. ---- ===== General Info ===== **Teacher**: * [[https://didattica.unicam.it/Guide/PaginaDocente.do?docente_id=3138|Prof. Massimo Callisto De Donato]] * Webex room: https://unicam.webex.com/meet/massimo.callisto * Email: massimo.callisto[at]unicam.it **ESSE3 Link** * [[|Techonologies for Big Data Management - AY 2024/25]] **Scheduling of Lectures**: * Scheduling is available at the following [[:didattica:ay2425:orario_en|link]] **Degrees**: * [[didattica:mscs|MSc in Computer Science (LM-18)]] ---- ===== Course Objectives ===== * knowledge about business data analysis and modern scenarios such as the Internet of Things. * Understanding of main differences between classical methods in data analysis and new modern scenarios. * Knowledge and expertise on Big Data methodologies and technologies, basic principles and concepts, techniques that enable data analysis and management. * Knowledge of the main Big Data technological frameworks and application in real case studies. * Highlight some Intelligent Data Analysis techniques. ---- ===== Syllabus ===== *Introduction to enterprise and data management. *Analysis of scenarios and contexts of data generation: from the inter-organizational model to the Internet of things. *Introduction to the classic data analysis techniques: ETL (Extract, Transform, Load), Business Intelligence, Reporting Tools. *Methodologies and technologies for the management and analysis of large amounts of data: introduction to the Big Data model, concepts, principles and technological frameworks. *Data Analytics methodologies and techniques: batch analysis models, streaming computation. *Data Analytics evolution towards intelligent data understanding models. ---- ===== Study material ===== **Course Slides** * Slides {{ https://drive.google.com/drive/folders/1tcpUqLRvOnPeAJXHSAqVpLvfv6vZ1DvI?usp=sharing | link}} * Github {{ https://github.com/massimocallisto | link }} * Webex recordings {{ https://docs.google.com/document/d/1JYz2CP5fzmcMjYVzYw8S4NNfHTirVYdF4AwWyJ9C14g/edit?usp=sharing | link}} * Projects {{ https://drive.google.com/drive/folders/1gFg_sGXI-29ClcwHuqW9BfHOXKHeEYhG | link}} * TBDM [[http://didattica.cs.unicam.it/doku.php?id=didattica:ay2324:tbdm:main | A.A.2023/24]] * **Reference materials** * Slides course. * Material provided by the teacher. * Examples * ... * Other materials * ... ---- ===== Exams ===== **Exam Dates A.Y. 2024/2025 (tentative)** * 03/02/2025 * 24/02/2025 * 23/06/2025 * 07/07/2025 * 28/07/2025 * 08/09/2025 * 29/09/2025 * 02/02/2026 * 16/02/2026 **Exam rules**: * Writing Examination * Project lab (max 3 member per team) For the discussion students have to publish the final code to a public github repository (or similar) along with the necessary documnatation (pre-requisite, installation, configuration, usage, etc). Moreover a set of slides have to be presented in that day along with a practical demonstration of the running code. Slides should cover the following points: - Project description / objectives - Methodology and technology being used - Technical implementation - Achieved results - future improvements ** Exam Results ** * See news section