Supporting the analysis of urban data through NoSQL technologies

In the last few years, the capability to both generate and collect data of public interest within the urban area has increased at an unprecedented rate, to such an extent that data rapidly scale towards big urban data. The abundance of information collected through ad-hoc sensors networks in the smart city context provides a remarkable opportunity to tackle interesting urban challenges and to add intelligences in the urban environment. However, for each data source and type, different spatial and temporal references are potentially used. Hence, the complexity of dealing with such an heterogeneity of data has significantly increased. This paper proposes a distributed business intelligence engine, named BI2CITY, able to efficiently manage the process of integrating and analysing a large volume of heterogeneous data generated by various sources in the smart city context. BI2CITY exploits a Big Data approach to support (i) data storage, (ii) spatio-temporal data aggregation, and (iii) different targeted data analyses, such as correlating urban data and forecasting the expected values of some interesting data (e.g., air pollution). Spatio-temporal data aggregation and analyses are performed on the fly using MapReduce based algorithms. Experimental results on real data collected in a major Italian city demonstrate the effectiveness of the proposed distributed system to perform interesting and efficient analysis.