2016
Conference article  Restricted

Big data and public administration: a case study for Tuscany airports

Furletti B., Nanni M., Fadda D., Piccini L., Lattarulo P.

GPS  Data analysis  Mobility data mining  Database Applications  Data mining 

In the last decade, the fast development of Information and Communication Technologies led to the wide diffusion of sensors able to track various aspects of human activity, as well as the storage and computational capabilities needed to record and analyze them. The so-called Big Data promise to improve the effectiveness of businesses, the quality of urban life, as well as many other fields, including the functioning of public administrations. Yet, translating the wealth of potential information hidden in Big Data to consumable intelligence seems to be still a difficult task, with a limited basis of success stories. This paper reports a project activity centered on a public administration - IRPET, the Regional Institute for Economic Planning of Tuscany (Italy). The paper deals, among other topics, with human mobility and public transportation at a regional scale, summarizing the open questions posed by the Public Administration (PA), the envisioned role that Big Data might have in answering them, the actual challenges that emerged in trying to implement them, and finally the results we obtained, the limitations that emerged and the lessons learned.

Source: SEBD 2016 - 24th Italian Symposium on Advanced Database Systems, pp. 158–165, Ugento, Lecce, 19-22 giugno 2016



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Projects (via OpenAIRE)

PETRA
Personal Transport Advisor: an integrated platform of mobility patterns for Smart Cities to enable demand-adaptive transportation systems


OpenAIRE
BibTeX entry
@inproceedings{oai:it.cnr:prodotti:357993,
	title = {Big data and public administration: a case study for Tuscany airports},
	author = {Furletti B. and Nanni M. and Fadda D. and Piccini L. and Lattarulo P.},
	booktitle = {SEBD 2016 - 24th Italian Symposium on Advanced Database Systems, pp. 158–165, Ugento, Lecce, 19-22 giugno 2016},
	year = {2016}
}