Discovering Information from Spatial Big Data for ITS

Modern vehicles include a wide variety of sensors. With the arrival on the market of connected cars, information collected from these sensors can be potentially shared with a remote data center in real-time. This could give rise to one of the largest sensor networks in the world, harvesting gigabytes per second, which can be referred to as Spatial Big Data (SBD). The number and type of applications that could be generated on top of the knowledge extracted from the data coming by vehicles’ sensors is almost limitless, ranging from much more accurate Intelligent Transportation Systems (ITS), to better weather forecast, insight on the geographical distribution of the pollutants, and so on. On the other hand, this scenario will pose severe challenges to the practitioner in the field of Information and Communication Technologies (ICT), since the amount of SBD that could be collected by the sensor network of connected cars will by far exceed the storage and processing capacity of commonly computing and database technologies. Therefore, the key tasks of the Knowledge Discovery Process (KDP), mainly data storage and data mining, should be significantly revised in order to be able to properly handle and exploit such a big amount of spatial data. In this chapter, we discuss the current state of the art and we provide an overview of the main challenges that must be faced in the KDP domain, in order to have an infrastructure ready to deal with SBD coming from the sensors of the connected cars, and to extract new knowledge able to generate novel and exciting applications.