Graph Constraints in Urban Computing: Dealing with Conditions in Processing Urban Data

Abstract

Smart Cities is a worldwide initiative leading to better exploit the resources in a city in order to offer higher level services to people. In this context, urban computing is a process of acquisition, integration, and analysis of big and heterogeneous data generated by a diversity of sources in urban spaces, such as sensors, traffic devices, vehicles, buildings, and humans, to tackle the major issues that cities face, e.g. air pollution, increased energy consumption and traffic congestion. The majority of these information can be represented as graphs, such as the transportation network, in which places (nodes) are connected by some form of public transportation (edges). A vision of the "city of the future", or even the city of the present, rests on the integration of science and technology through information systems. This vision requires a rethinking of the relationships between technology, government, city managers, business, academia and the research community. This position paper presents our views towards developing techniques for querying and evolving graph-modeled datasets based on userdefined constraints. Our focus is to show how these techniques can be applied to effectively retrieve urban data and have automated mechanisms that guarantee data consistency.