|Big Data Analytics|
|Total hours: 10|
The aim of this course is to give students knowledge about big data analytics architectures and help them to understand when and how to appropriately use such scalable data processing solutions. The course will help students understand the challenges and opportunities of big data analytic infrastructures.
After successfully completing this course the students should be able to:
1. Understand the challenges and opportunities in dealing with Big Data
2. Understand the on-going development of Big Data infrastructure solutions for Volume, Variety, and Velocity including industry-driven and open-source solutions
3. Apply data analytics infrastructures to best support data science practices for non-technical stakeholders (e.g., executives)
4. Judge in which situations Big Data analytics solutions are more or less appropriate
This course aims to:
1. Provide an introduction to different computational architectures for big data and algorithms (e.g., Map/Reduce);
2. Provide an overview of existing big data analytics products for volume, velocity, and variety of data;
3. Show how big data analytics is used in industry by means of use cases;
Learning evaluation methods
Given a dataset, students should use big data analytics techniques to explore the data and to draw some conclusions that inform decision makers. Students should write a 1,500 word structured report that describes the approach taken to analyse the given dataset using big data analytics techniques. The report should focus on summarising the approach on the given dataset and presenting the main findings.
|Probabilistic Logic Programming, Probabilistic Description Logics and their applications|
|Total hours: 14|
- Agostino Dovier: Introduction to Logic Programming
- Fabrizio Riguzzi: Introduction to probabilistic logic programming: Syntax and Semantics
- Elena Bellodi: Probabilistic logic programming: review of sintax and semantics, inference
- Elena Bellodi: Probabilistic logic programming: learning and applications
- Riccardo Zese: Description Logics Probabilistiche: syntax and semantics
- Riccardo Zese: Description Logics Probabilistiche: inference, learning, applications, and combination with probabilistic logic programming.
- Fabrizio Riguzzi. Foundations of Probabilistic Logic Programming River Publishers, 2018.
- Riccardo Zese. Probabilistic Semantic Web: Reasoning and Learning IOS PRESS, 2017
- A. Dovier and E. Pontelli eds. A 25 Year Perspective on Logic Programming LNCS vol 6125, Springer Verlag, 2010.
- A. Dovier and A. Formisano. Programmazione Dichiarativa con Prolog, CLP, ASP, e CCP
- J.W. Lloyd. Foundations of Logic Programming 2nd edition, Springer Verlag, 1987.
- K.R. Apt. From Logic Programming to Prolog Prentice Hall, 1997.
|Web tools for supporting scientific research and dissemination|
|Total hours: 12|
The aim of this course is to present a set of Web tools (applications, and scientific and professional social networks), which offer new challenging opportunities for supporting the research and the scientific dissemination. The participants will be work in computer lab for modelling and realizing multimedia digital artefacts, and for enhancing the modalities of scientific communication on the Web.
This course is organized in two main parts. The first part, more general, introduces students to cloud computing, Web applications, and professional social networks dedicated to scientific dissemination. The second part, mainly proposed in computer lab, involves the students:
- in the creation of a set of digital products, conceived to enhance professional activities and initiatives. Students will use a set of applications to prepare presentations and infographics; interactive images and videos; conceptual maps; timelines and other digital artefacts.
- in defining and managing their digital identity on professional (such as LinkedIn) and dedicated to scientific dissemination (such as ResearchGate, Mendeley, Academia) social networks.
Frontal lessons and and lab practice.
Learning evaluation methods
Students will be required to deepen the content of the course and apply them in concrete case study. They will present their contribution in a short seminar and in a report, containing all the realized digital artefacts.