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LNBIP reports state-of-the-art results in areas related to business information systems and industrial application software development -timely, at a high level, and in both printed and electronic form. The type of material published includes• Proceedings (published in time for the respective event)• Postproceedings (consisting of thoroughly revised and/or extended final papers) • Other edited monographs (such as, for example, project reports or invited volumes) • Tutorials (coherently integrated collections of lectures given at advanced courses, seminars, schools, etc.) • Award-winning or exceptional theses LNBIP is abstracted/indexed in DBLP, EI and Scopus. LNBIP volumes are also submitted for the inclusion in ISI Proceedings.

Industry 4.0 is nowadays the reference paradigm for production system implementation. The reasons lay in several motivations, among which the product/process data availability. This is paramount in supporting product tracking and tracing, feeding optimization applications, enabling sophisticated maintenance approaches and in monitoring resources and energy consumption in a sustainability perspective. While the setup of "green field" implementations is usually easier and well defined, problems arise when there is an existing system with physical shop-floor devices, applications and on-going production processes that cannot be disturbed or interrupted, and need to be interfaced. This paper is aiming to define an implementation strategy and a system architecture able to upgrade an existing production system to "Industry 4.0 compliant" status, keeping into account features and characteristics of said system, and applications without direct intervention (software or hardware) on the system and without perturbations of any on-going business. The proposed AI40A (Additive I40 Architecture) is structured on three basic components of: Data collection, Data transfer and Condition detection and trend forecasting. Each component and sub-module can be relocated individually on physical servers, cloud or edge computing virtual machines, based on availability or resources, computational needs or security reasons. As proof of concept, a prototype of the Additive Industry 4.0 Architecture that is implemented in Industry 4.0 Lab (I4.0Lab) of the School of Management of Politecnico di Milano in Bovisa (Milano) Campus will be shown. Two industrial applications are currently deployed on top of it: Production Rescheduling on Condition and Prognostic Maintenance based on Condition Regression with AR (Augmented Reality) support.

The main aim of the FENIX project is the development of new business models and industrial strategies for three novel supply chains in order to enable value-added product-services. Through a set of success stories coming from the application of circular economy principles in different industrial sectors, FENIX wants to demonstrate in practice the real benefits coming from its adoption. In addition, Key Enabling Technologies (KETs) will be integrated within the selected processes to improve the efficient recovery of secondary resources. In this sense, among the available KETs, the adoption of digital and advanced automated solutions allows companies to re-thinking their business strategies, trying to cope with even more severe environmental requirements. Among these technological solutions, the paradigm of Industry 4.0 (I4.0) is the most popular. I4.0 entails the development of a new concept of economic policy based on high-tech strategies and internet-connected technologies allowing the creation of added-value for organizations and society. Unlike the activities developed in T3.1, related to the development and implementation of simulation tools and models for the smartphones’ disassembly process optimization, here the attention has been spent in managing and optimizing a new semi-automated PCBs disassembly station. The disassembly of products is a key process in the treatment of Waste Electrical and Electronic Equipment. When performed efficiently, it enables the maximization of resources re-usage and a minimization of pollution. Within the I4.0 paradigm, collaborative robots (co-bots in short) can safely interact with humans and learn from them. This flexibility makes them suitable for supporting current CE practices, especially during disassembly and remanufacturing operations. D3.2 focuses on describing the semi-automated PCB disassembly process implemented at the POLIMI’s Industry 4.0 Lab, aiming to demonstrate in practice the benefits of exploiting I4.0 technologies in PCB disassembly processes. Results highlight how a semi-automated cell where operators and cobots works together can allow a better management of both repetitive and specific activities, the safe interaction of cobots with operators and the simple management of the high variability related with different kinds of PCBs.

Knowledge bases like DBpedia, Yago or Google's Knowledge Graph contain huge amounts of ontological knowledge harvested from (semi-)structured, curated data sources, such as relational databases or XML and HTML documents. Yet, the Web is full of knowledge that is not curated and/or structured and, hence, not easily indexed, for example social data. Most work so far in this context has been dedicated to the extraction of entities, i.e., people, things or concepts. This paper describes our work toward the extraction of relationships among entities. The objective is reconstructing a typed graph of entities and relationships to represent the knowledge contained in social data, without the need for a-priori domain knowledge. The experiments with real datasets show promising performance across a variety of domains.