The ability to describe business processes as executable models has always been one of the fundamental premises of workflow management. Yet, the tacit nature of human knowledge is often an obstacle to eliciting accurate process models. On the other hand, the result of process modeling is a static plan of action, which is difficult to adapt to changing procedures or to different business goals. In this article, we attempt to address these problems by approaching workflow management with a combination of learning and planning techniques. Assuming that processes cannot be fully described at build-time, we make use of learning techniques, namely Inductive Logic Programming (ILP), in order to discover workflow activities and to describe them as planning operators. These operators will be subsequently fed to a partial-order planner in order to find the process model as a planning solution. The continuous interplay between learning, planning and execution aims at arriving at a feasible plan by successive refinement of the operators. The approach is illustrated in two simple scenarios. Following a discussion of related work, the paper concludes by presenting the main challenges that remain to be solved.

This study investigated the microleakage of two different root canal obturation systems, using the nuclear medicine approach, with sodium pertechnetate 99m Tc. Twenty six single-rooted extracted teeth were selected. The crowns were sectioned to obtain 15 mm long root segments and each tooth was prepared using rotary ProFile ® instruments. The roots were divided into 2 experimental groups and two control groups. Twenty root canals were filled, using Thermafil ® and Topseal ® or MTA Fillapex ® as a sealer. On the 7th and the 28th day the apices were submersed in a solution of 99m Tc-Pertechnetate during 3 hours. The radioactivity was counted using a gamma camera. Although apical leakage on the 7th day in the Topseal group was reduced compared with RealSeal1, with a statistical significant difference (p = 0.057), on the 28th day, the MTA Fillapex increased the sealing properties (p = 0.017).

-The reliability and safety of industrial machines depends on their timely maintenance. The integration of Cyber Physical Systems within the maintenance process enables both continuous machine monitoring and the application of advanced techniques for predictive and proactive machine maintenance. The building blocks for this revolution -embedded sensors, efficient preprocessing capabilities, ubiquitous connection to the internet, cloud-based analysis of the data, prediction algorithms, and advanced visualization methods -are already in place, but several hurdles have to be overcome to enable their application in real scenarios, namely: the integration with existing machines and existing maintenance processes. Current research and development efforts are building pilots and prototypes to demonstrate the feasibility and the merits of advanced maintenance techniques, and this paper describes a system for the industrial maintenance of sheet metal working machinery and its evolution towards a full proactive maintenance system.

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