Suchergebnisse

Awareness of the need to provide digital preservation solutions is spreading from the core memory institutions to other domains, including government, industry, SME and consumers. In many of these settings we are, however, faced with preserving more than just data. In the domain of eScience, for example, investigations are increasingly collaborative. Most scientific and engineering domains benefit from building on the outputs of other research by sharing information to reason over and data to incorporate in the modeling task at hand. This raises the need for preserving and sharing entire eScience workflows and processes for later reuse. We need to define which information is to be collected, create means to preserve it and approaches to enable and validate the re-execution of a preserved process. This includes and goes beyond preserving the data used in the experiments, as the process underlying its creation and use is essential. The TIMBUS project and Wf4Ever project team up for this halfday tutorial to provide an introduction to the problem domain and discuss solutions for the curation of eScience processes.

AbstractMultiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.