During the last years, smart cities have attracted considerable attention because they are considered a response to the complex challenges that modern cities face. Smart cities provide new solutions in the domains of mobility, environment, economy, governance, quality of life, and education thanks to the innovative use of Information and Communication Technologies (ICT). However, smart cities often do not optimally reach their objectives if the citizens, the end-users, are not involved in their design. This article investigates how the citizens can transform a city into a smart city by being involved in the democratic process, by cocreating the smart city projects, and by proactively using the city's ICT infrastructure. This article contributes to the smart city domain in several ways. Firstly, a literature review from different research fields is performed in order to identify the different means to enable citizen participation in a smart city. Secondly, a framework to compare and evaluate smart cities as enablers of citizen participation is proposed, based on the findings of the preceding section. Finally, the framework is applied to the ongoing smart city design of Namur (Belgium), allowing drawbacks and flaws in citizens' participation to be discovered and improved.

Sequence classification deals with the task of finding discriminative and concise sequential patterns. To this purpose, many techniques have been proposed, which mainly resort to the use of partial orders to capture the underlying sequences in a database according to the labels. Partial orders, however, pose many limitations, especially on expressiveness, i.e. the aptitude towards capturing certain behavior, and on conciseness, i.e. doing so in a compact and informative way. These limitations can be addressed by using a better representation. In this paper we present the interesting Behavioral Constraint Miner (iBCM), a sequence classification technique that discovers patterns using behavioral constraint templates. The templates comprise a variety of constraints and can express patterns ranging from simple occurrence, to looping and position-based behavior over a sequence. Furthermore, iBCM also captures negative constraints, i.e. absence of particular behavior. The constraints can be discovered by using simple string operations in an efficient way. Finally, deriving the constraints with a window-based approach allows to pinpoint where the constraints hold in a string, and to detect whether patterns are subject to concept drift. Through empirical evaluation, it is shown that iBCM is better capable of classifying sequences more accurately and concisely in a scalable manner.