The user support programme and the training infrastructure of the EGI Federated Cloud

The EGI Federated Cloud is a standards-based, open cloud system as well as its enabling technologies that federates institutional clouds to offer a scalable computing platform for data and/or compute driven applications and services. The EGI Federated Cloud is based on open standards and open source Cloud Management Frameworks and offers to its users IaaS, PaaS and SaaS capabilities and interfaces tuned towards the needs of users in research and education. The federation enables scientific data, workloads, simulations and services to span across multiple administrative locations, allowing researchers and educators to access and exploit the distributed resources as an integrated system. The EGI Federated Cloud collaboration established a user support model and a training infrastructure to raise visibility of this service within European scientific communities with the overarching goal to increase adoption and, ultimately increase the usage of e-infrastructures for the benefit of the whole European Research Area. The paper describes this scalable user support and training infrastructure models. The training infrastructure is built on top of the production sites to reduce costs and increase its sustainability. Appropriate design solutions were implemented to reduce the security risks due to the cohabitation of production and training resources on the same sites. The EGI Federated Cloud educational program foresees different kind of training events from basic tutorials to spread the knowledge of this new infrastructure to events devoted to specific scientific disciplines teaching how to use tools already integrated in the infrastructure with the assistance of experts identified in the EGI community. The main success metric of this educational program is the number of researchers willing to try the Federated Cloud, which are steered into the EGI world by the EGI Federated Cloud Support Team through a formal process that brings them from the initial tests to fully exploit the production resources. © 2015 IEEE

Special Session on Industry 4.0

No abstract available

The OCareCloudS project: toward organizing care through trusted cloud services

The increasing elderly population and the shift from acute to chronic illness makes it difficult to care for people in hospitals and rest homes. Moreover, elderly people, if given a choice, want to stay at home as long as possible. In this article, the methodologies to develop a cloud-based semantic system, offering valuable information and knowledge-based services, are presented. The information and services are related to the different personal living hemispheres of the patient, namely the daily care-related needs, the social needs and the daily life assistance. Ontologies are used to facilitate the integration, analysis, aggregation and efficient use of all the available data in the cloud. By using an interdisciplinary research approach, where user researchers, (ontology) engineers, researchers and domain stakeholders are at the forefront, a platform can be developed of great added value for the patients that want to grow old in their own home and for their caregivers

The OCarePlatform : a context-aware system to support independent living

Background: Currently, healthcare services, such as institutional care facilities, are burdened with an increasing number of elderly people and individuals with chronic illnesses and a decreasing number of competent caregivers. Objectives: To relieve the burden on healthcare services, independent living at home could be facilitated, by offering individuals and their (in)formal caregivers support in their daily care and needs. With the rise of pervasive healthcare, new information technology solutions can assist elderly people ("residents") and their caregivers to allow residents to live independently for as long as possible. Methods: To this end, the OCarePlatform system was designed. This semantic, data-driven and cloud based back-end system facilitates independent living by offering information and knowledge-based services to the resident and his/her (in)formal caregivers. Data and context information are gathered to realize context-aware and personalized services and to support residents in meeting their daily needs. This body of data, originating from heterogeneous data and information sources, is sent to personalized services, where is fused, thus creating an overview of the resident's current situation. Results: The architecture of the OCarePlatform is proposed, which is based on a service-oriented approach, together with its different components and their interactions. The implementation details are presented, together with a running example. A scalability and performance study of the OCarePlatform was performed. The results indicate that the OCarePlatform is able to support a realistic working environment and respond to a trigger in less than 5 seconds. The system is highly dependent on the allocated memory. Conclusion: The data-driven character of the OCarePlatform facilitates easy plug-in of new functionality, enabling the design of personalized, context-aware services. The OCarePlatform leads to better support for elderly people and individuals with chronic illnesses, who live independently. (C) 2016 Elsevier Ireland Ltd. All rights reserved

Towards FollowMe User Profiles for Macro Intelligent Environments

We envision an Ambient Intelligent Environment as an environment with technology embedded within the framework of that environment to help enhance an users experience in that environment. Existing implementations , while working effectively, are themselves an expensive and time consuming investment. Applying the same expertise to an environment on a monolithic scale is very inefficient, and thus, will require a different approach. In this paper, we present this problem, propose theoretical solutions that would solve this problem, with the guise of experimentally verifying and comparing these approaches, as well as a formal method to model the entire scenario