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EnglishRecent advances in sensor-equipped smartphones are opening brand new opportunities, such as automatically extracting Points Of Interest (POIs) and mobility habits of citizens in Smart Cities from the large amount of harvested data hotspots. At the same time, the high dynamicity and unpredictability of Smart Cities crowds, opportunistically collaborating toward these common crowdsensing tasks, introduces challenging issues due to the need for fast and continuous processing of these Big Data Streams in the backend of next generation crowdsensing platforms. This paper presents our practical experiences and lessons learnt in deploying the ParticipAct platform and living lab, an ongoing experiment at University of Bologna that involves 300 students for one year. Among all management issues addressed in ParticipAct, this article shows the integration of MongoDB in the ParticipAct backend, as a powerful NoSQL storage and processing engine to fasten the identification of POIs; the reported performance results confirm the feasibility of the approach by quantifying its advantages for city managers.
| Titolo: | Automatic extraction of POIs in smart cities: Big data processing in ParticipAct | |
| Autore/i: | CORRADI, ANTONIO; Curatola, Giovanni; FOSCHINI, LUCA; IANNIELLO, RAFFAELE; De Rolt, Carlos Roberto | |
| Autore/i Unibo: | ||
| Anno: | 2015 | |
| Titolo del libro: | Proceedings of the 2015 IFIP/IEEE International Symposium on Integrated Network Management, IM 2015 | |
| Pagina iniziale: | 1059 | |
| Pagina finale: | 1064 | |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1109/INM.2015.7140433 | |
| Abstract: | Recent advances in sensor-equipped smartphones are opening brand new opportunities, such as automatically extracting Points Of Interest (POIs) and mobility habits of citizens in Smart Cities from the large amount of harvested data hotspots. At the same time, the high dynamicity and unpredictability of Smart Cities crowds, opportunistically collaborating toward these common crowdsensing tasks, introduces challenging issues due to the need for fast and continuous processing of these Big Data Streams in the backend of next generation crowdsensing platforms. This paper presents our practical experiences and lessons learnt in deploying the ParticipAct platform and living lab, an ongoing experiment at University of Bologna that involves 300 students for one year. Among all management issues addressed in ParticipAct, this article shows the integration of MongoDB in the ParticipAct backend, as a powerful NoSQL storage and processing engine to fasten the identification of POIs; the reported performance results confirm the feasibility of the approach by quantifying its advantages for city managers. | |
| Data stato definitivo: | 16-lug-2016 | |
| Appare nelle tipologie: | 4.01 Contributo in Atti di convegno |
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http://hdl.handle.net/11585/554173
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