Next Generation Cloud Computing: New Trends and Research Directions

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201

Analytic Black Holes: a data-oriented perspective

In Analytic Black Holes it is advocated to start a new, second, revolution in security and intelligence analysis. After the first revolution, which started in the Netherlands as late as 2006 with the massive training in Structured Analytic Techniques at both the academia and at the MoD (e.g. Defense Intelligence and Security Institute). A new second revolution – that of Augmented Intelligence – is at hand as a result of two developments, the change in data flows and the need for new products. Data are exploding, especially unstructured data. But the majority of the data remains unused in analyses. Furthermore, hybrid threats and real time intelligence for the protection of the critical infrastructure demand a new approach towards analysis. This gap needs to be filled, among others, by data science cells that can process data automatically. This way, a new analytic approach can be reached – that of Augmented Intelligence – in which humans and machines are paired in the analytic process. Human Analysis is likely to develop more to-wards to limit the number of data taken into account, but those data will have a high causal significance. Machine Analysis, on the other hand, will process huge amount of data and focus on correlations in the first place. Augmented Intelligence, will be a merger of both, that can manifest itself by different combinations of both. It will deal with data that now re-main unused. It can fill the gap of the identified analytic black holes. Dealing with the analytic black holes will enhance the security, and make us more effective in protecting our critical infrastructure

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions