Leveraging Relational Technology for Data-Centric Dynamic Systems Diego Calvanese, Marco Montali, Fabio Patrizi, Andrey Rivkin Free University of Bozen-Bolzano, Piazza Domenicani 3, 39100 Bolzano, Italy {calvanese,montali,patrizi,rivkin}@inf.unibz.it Abstract. We base our work on a model called data-centric dynamic system (DCDS), which can be seen as a framework for modeling and verification of systems where both the process controlling the dynamics and the manipulation of data are equally central. More specifically, a DCDS consists of a data layer and a process layer, interacting as follows: the data layer stores all the data of interest in a relational database, and the process layer modifies and evolves such data by executing actions under the control of a process, and possibly injecting into the system external data retrieved through service calls. In this work, we propose an implementation of DCDSs in which all aspects concerning not only the data layer but also the process layer, are realized by means of functionalities provided by a relational DBMS. We present the architecture of our prototype system, describe its functionality, and discuss the next steps we intend to take towards realizing a full-fledged DCDS-based system that supports verification of rich temporal properties. 1 Introduction Modeling and analyzing the correctness of today’s complex business processes is a very challenging task, that touches on the one side the management of static (data-related) aspects, and on the other side dynamic (process-related) concerns. Traditional approaches deal with these two pillars separately, and this divide et impera approach has led to the development of successful theories and technologies, such as: – databases, ontologies and information integration to account for static aspects; – business process management, service-oriented computing, formal verification and model checking for dynamic ones. However, it has been extensively argued that this separation prevents business experts and analysts from understanding the organization as a whole, and of taking correspond- ing strategic decisions [13,18]. Therefore, more recently these two aspects have been addressed together, and this has lead to a flourishing literature dealing with the formal foundations [4,5] of data-aware (business) processes, as well as languages [15,12,16], and integrated software platforms [14] for modeling and running them. In this spectrum, an important dimension regards whether the process works over a data component that is assumed to completely or only partially capture the domain knowledge. In this work, we focus on complete information, and consider in particular the framework of data-centric dynamic systems (DCDSs) [2], which tackles modeling and verification of data-aware processes running over a full-fledged relational database