We present the design and evaluation of a Datalog engine for execution in Graphics Processing Units (GPUs). The engine evaluates recursive and non-recursive Datalog queries using a bottom-up approach based on typical relational operators. It includes a memory management scheme that automatically swaps data between memory in the host platform (a multicore) and memory in the GPU in order to reduce the number of memory transfers. To evaluate the performance of the engine, four Datalog queries were run on the engine and on a single CPU in the multicore host. One query runs up to 200 times faster on the (GPU) engine than on the CPU.

This paper reports the work in progress towards the specification of a conceptual architecture of a smart system for supporting the management of disruptions in the manufacturing domain. In particular, it proposes an approach to the description of the system architecture based on a number of interrelated viewpoints following the pertinent ISO 42010 standard. The approach is being developed in the context of the EU-funded H2020 DISRUPT project aiming to deliver a comprehensive datadriven solution for automated vertical and horizontal integration facilitating the transition into smart manufacturing.

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Relational learning algorithms mine complex databases for interesting patterns. Usually, the search space of patterns grows very quickly with the increase in data size, making it impractical to solve important problems. In this work we present the design of a relational learning system, that takes advantage of graphics processing units (GPUs) to perform the most time consuming function of the learner, rule coverage. To evaluate performance, we use four applications: a widely used rela

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