Manufacturing Data Analytics for Manufacturing Quality Assurance

The authors acknowledge the European Commission for the support and funding under the scope of Horizon2020 i4Q Innovation Project (Agreement Number 958205) and the remaining partners of the i4Q Project Consortium.Nowadays, manufacturing companies are eager to access insights from advanced analytics, without requiring them to have specialized IT workforce or data science advanced skills. Most of current solutions lack of easy-to-use advanced data preparation, production reporting and advanced analytics and prediction. Thanks to the increase in the use of sensors, actuators and instruments, European manufacturing lines collect a huge amount of data during the manufacturing process, which is very valuable for the improvement of quality in manufacturing, but analyzing huge amounts of data on a daily basis, requires heavy statistical and technology training and support, making them not accessible for SMEs. The European i4Q Project, aims at providing an IoT-based Reliable Industrial Data Services (RIDS), a complete suite consisting of 22 i4Q Solutions, able to manage the huge amount of industrial data coming from cheap cost-effective, smart, and small size interconnected factory devices for supporting manufacturing online monitoring and control. This paper will present a set of i4Q services, for data integration and fusion, data analytics and data distribution. Such services, will be responsible for the execution of AI workloads (including at the edge), enabling the dynamic deployment industrial scenarios based on a cloud/edge architecture. Monitoring at various levels is provided in i4Q through scalable tools and the collected data, is used for a variety of activities including resource monitoring and management, workload assignment, smart alerting, predictive failure and model (re)training.publishersversionpublishe

Supplemental Material - Towards a personalized health care using a divisive hierarchical clustering approach for comorbidity and the prediction of conditioned group risks

Supplemental Material for Towards a personalized health care using a divisive hierarchical clustering approach for comorbidity and the prediction of conditioned group risks by J Ramón Navarro-Cerdán, Manuel Sánchez-Gomis, Patricia Pons, Santiago Gálvez-Settier, Francisco Valverde, Ana Ferrer-Albero, Inmaculada Saurí, Antonio Fernández and Josep Redon in Health Informatics Journal</p

General Learnings from the Horizon 2020 Project BigMedilytics

Big Data, in combination with Artificial Intelligence (AI), has the potential to change and improve processes in medicine. However, these activities/technologies must be developed to promote the trust of all stakeholders: patients, healthcare professionals, private and public providers and businesses. Providing a Trustworthy AI - lawful, ethical and robust - requires significant efforts. Although technological development is moving quickly, testing, validation and integration of such innovation may take many years. The reasons which slow down this process are manifold. However, some barriers and pitfalls are foreseeable and, therefore, can be taken into account or avoided. In order to support future development and integration of AI and Big Data technologies, we present technical challenges and lessons learnt from our previous project, BigMedilytics, involving clinicians and data scientists. This chapter considers the challenges data scientists providing advanced technology in the healthcare domain may face, along with some suggestions to address any related issues.f applicabl