Big Data Sensing & Procurement

Information Retrieval

Credits: 
4
Hours: 
42
Area: 
Big Data Sensing & Procurement
Teachers: 
Academic Year: 
Description: 

The module provides the description of a search engine structure and of Text Mining tools, by analyzing their characteristics and limits with respect to the computational cost, the precision/recall/F1 parameters, and the expressivity of the supported queries. The module is also based on hands-on activities that will present well-known open-source Python tools for the crawling and analysis of web pages, the semantic annotation of texts (TagMe), and the indexing of text data collections (ElasticSearch).

Big data sources, crowdsourcing, crowdsensing

Credits: 
2
Hours: 
20
Area: 
Big Data Sensing & Procurement
Teachers: 
Academic Year: 
Description: 

This module presentes techniques and methods for acquisition of Big Data from a large sources of data available, including mobile phone data, GPS data, customer purchase data, social network data, open and administrative data, environmental and personal sensor data. We discuss also several participatory methods for crowdsourcing or crowdsensing collection of data through ad hoc campains like serious games and viral diffusion.

Information Retrieval

Credits: 
4
Hours: 
40
Area: 
Big Data Sensing & Procurement
Teachers: 
Academic Year: 
Description: 

The module aims to teach the software modules used to build a modern search engine and the analysis of the performance and the computational limits of the algorithmics solutions currently used in each of them. Practical and theoretical fundmentals for the organization and the analysis of IR systems.

Information Retrieval

Credits: 
4
Hours: 
40
Area: 
Big Data Sensing & Procurement
Teachers: 
Tutor: 
Academic Year: 
Description: 

The module aims to teach the software modules used to build a modern search engine and the analysis of the performance and the computational limits of the algorithmics solutions currently used in each of them. Practical and theoretical fundmentals for the organization and the analysis of IR systems.

Big data sources, crowdsourcing, crowdsensing

Credits: 
2
Hours: 
20
Area: 
Big Data Sensing & Procurement
Teachers: 
Academic Year: 
Description: 

This module presentes techniques and methods for acquisition of Big Data from a large sources of data available, including mobile phone data, GPS data, customer purchase data, social network data, open and administrative data, environmental and personal sensor data. We discuss also several participatory methods for crowdsourcing or crowdsensing collection of data through ad hoc campains like serious games and viral diffusion.

Big data sources, crowdsourcing, crowdsensing

Credits: 
2
Hours: 
20
Area: 
Big Data Sensing & Procurement
Teachers: 
Academic Year: 
Description: 

This module presentes techniques and methods for acquisition of Big Data from a large sources of data available, including mobile phone data, GPS data, customer purchase data, social network data, open and administrative data, environmental and personal sensor data. We discuss also several participatory methods for crowdsourcing or crowdsensing collection of data through ad hoc campains like serious games and viral diffusion.

Information Retrieval

Credits: 
5
Hours: 
40
Area: 
Big Data Sensing & Procurement
Teachers: 
Tutor: 
Academic Year: 
Description: 

The module aims to teach the software modules used to build a modern search engine and the analysis of the performance and the computational limits of the algorithmics solutions currently used in each of them. Practical and theoretical fundmentals for the organization and the analysis of IR systems.

Web Search Engines and Information Retrieval

Credits: 
3
Hours: 
21
Area: 
Big Data Sensing & Procurement
Academic Year: 
Description: 

The module aims to teach the software modules used to build a modern search engine and the analysis of the performance and the computational limits of the algorithmics solutions currently used in each of them. Practical and theoretical fundmentals for the organization and the analysis of IR systems.

Information Retrieval

Credits: 
5
Hours: 
40
Area: 
Big Data Sensing & Procurement
Teachers: 
Academic Year: 
Description: 

The module aims to teach the software modules used to build a modern search engine and the analysis of the performance and the computational limits of the algorithmics solutions currently used in each of them. Practical and theoretical fundmentals for the organization and the analysis of IR systems.

Big data sources, crowdsourcing, crowdsensing

Credits: 
2
Hours: 
20
Area: 
Big Data Sensing & Procurement
Teachers: 
Academic Year: 
Description: 

This module presentes techniques and methods for acquisition of Big Data from a large sources of data available, including mobile phone data, GPS data, customer purchase data, social network data, open and administrative data, environmental and personal sensor data. We discuss also several participatory methods for crowdsourcing or crowdsensing collection of data through ad hoc campains like serious games and viral diffusion.

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