The increasing smarteness of personal devices and territories are rapidly changing the scenarios of data production. Data access and/or data possession can determine positions of privilege, but the real competitive advantage will derive more and more from the ability to identify relevant dimensions and flows and, of course, from that to extract meaningful multidimensional descriptions aimed at detecting and foreseeing emergent behaviors (particularly relevant those concerning small communities) or, in other words, from the ability to transform "big data" into "smart data".Prerequisite for obtaining this goal is the identification of appropriate models and adequate spaces of representation that may inspire the development of appropriate strategies and methods of analysis. Few examples will be provided.
I. INTRODUCTIONWaiting for the advent of an usable and safe implementation of Internet-of-Things, able to transform "future visions" [1,2] into reality, at present information on the behavior of large amount of people can be collected in four different manners: a) by means of voluntary insertion of data (e.g. by filling forms or by performing on-line activities like when you use an ATM, obliterate a bus ticket, etc..); b) by means of sensors placed at key points of the flux "pipes" (e.g. energy or water consumption meters, detectors of environmental parameters, etc.); c) by associating appropriate transmitters of position to moving entities (e.g. cars, to detect the flow of traffic; waste, to follow the flow of waste recycling chains; goods, to monitor their movements, etc..); d) by means of personal devices that nowadays, due to their high level of penetration, smarteness and "always on" state, not only enable to locate the user through cell phone triangulation but also to get multidimensional information ranging from precise geolocation, to accelerations and orientations and, thanks to the cooperation of the individuals, to real time acquisition of any kind of multimedia signals.All data collected according to the above strategies are the result of individual actions but the big difference between the dataset produced by a), b), c) and most of those produce by d) resides in the different degree of access to the datasets. Those produced by a), b), c) are usually collected and owned by companies (service providers) that do not disclose the datasets because their possession is considered, in most cases, strategic; as consequence the knowledge is a prerogative of a few. Strategy d), on the other hand, produce datasets that, by means of suitable apps, are potentially accessible to anyone and could allow for a collective production of open datasets.