Big data sources, crowdsourcing, crowdsensing

Big Data Sensing & Procurement
Academic Year: 

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.


The course will give an overview of the main sources of Big Data characterized by the three "v": volumne, velocity, veracity. We will provide basic concpets to handle the complexity of crowdsourcing and participatory and opportunistic collections. Thorugh practice examples, we will also introduce relevant API available on the web to create mashup applications

Technics and tools: 

We will discuss the state-of-art techniques to handle Big Data, starting from the methods for data cleaning and integration to improve data quality. We will show techinques of data scraping and data acquisition via API available on the web.

Case studies and datasets: 

The objective of the course is to provide the capabilities to collect and create a collection of datasets from different sources, like for example social media (Twitter, Instagram, Facebook, Youtube, etc.), web sites, mobile phone data, GPS data, maps and spatial data, open data, administrative data, socio demographic data. We will present several case studies to create mashup applications starting from different services and analytical methods.


The course will provide an overview of methods and techinque to improve the skills to create and manage a large collection of crowdsourced data. We will introduce concept on the quality of data, data cleaning and tools for data integration and refinement. Practical excersises will introduce the main API available for social medias.