This course is concerned with one of the five issues (the 5 V's) which are core to big data, namely "variety". The goal of the course is to provide students with tools, methodologies, technologies needed in order to successfully analyse, model and integrate data coming from multiple heterogeneous sources.

COURSE DESCRIPTION
This course will cover the following topics:

• a general methodology for knowledge and data analysis, modeling and integration;
• an analysis of the state of the art tools and methodologies for data analysis, modeling and integration;
• an introduction to ontologies, Extended ER models and linguistic resources.

This is a hands-on, lab and experiment based course. Students will be given a data analysis/modelling/integration problem that they will have to solve, possibly, while taking the class. During the experiment, students will have to apply to the problem the notions introduced in class.

EXAM
The exam consists in addressing one of the items slots listed under "KDI project", where:

• Each title needs to be instantiated by a specific domain of knowledge - e.g., "Healthcare Domain Knowledge (DK) Analysis" or "Food Domain Knowledge (DK) Evolution"
• The final output consists on a written "Technical Report" (.doc) and, where requested, on a oral "Presentation" (.ppt)
• Each course project can be extended with a "Research project" and, eventually, with a "Thesis project", as described in the table below
• The items of a slot can be extended and/or combined with the items of another slot (in agreement with the teacher) - e.g., "DK Analysis" can be extended with "Create the OWL" model, which is present in "DK Generation"
• A more fine-grained description of the "Research project" and the "Thesis" project will be produced in agreement with the teacher