Future-generation healthcare systems will be highly distributed, combining centralised hospital systems with decentralised home-, work-and environment-based monitoring and diagnostics systems. These will reduce costs and injury-related risks whilst both improving quality of service, and reducing the response time for diagnostics and treatments made available to patients. To make this vision possible, medical data must be accessed and shared over a variety of mediums including untrusted networks. In this paper, we present the design and initial implementation of the SERUMS tool-chain for accessing, storing, communicating and analysing highly confidential medical data in a safe, secure and privacy-preserving way. In addition, we describe a data fabrication framework for generating large volumes of synthetic but realistic data, that is used in the design and evaluation of the tool-chain. We demonstrate the present version of our technique on a use case derived from the Edinburgh Cancer Centre, NHS Lothian, where information about the effects of chemotherapy treatments on cancer patients is collected from different distributed databases, analysed and adapted to improve ongoing treatments.

The proliferation of Big Data applications puts pressure on improving and optimizing the handling of diverse datasets across different domains. Among several challenges, major difficulties arise in data-sensitive domains like banking, telecommunications, etc., where strict regulations make very difficult to upload and experiment with real data on external cloud resources. In addition, most Big Data research and development efforts aim to address the needs of IT experts, while Big Data analytics tools remain unavailable to non-expert users to a large extent. In this paper, we report on the work-in-progress carried out in the context of the H2020 project I-BiDaaS (Industrial-Driven Big Data as a Self-service Solution) which aims to address the above challenges. The project will design and develop a novel architecture stack that can be easily configured and adjusted to address cross-sectoral needs, helping to resolve data privacy barriers in sensitive domains, and at the same time being usable by non-experts. This paper discusses and motivates the need for Big Data as a self-service, reviews the relevant literature, and identifies gaps with respect to the challenges described above. We then present the I-BiDaaS paradigm for Big Data as a self-service, position it in the context of existing references, and report on initial work towards the conceptual specification of the I-BiDaaS software architecture.

Parallelism in system architecture and design imposes a verification challenge as the exponential growth in the number of execution combinations becomes unwieldy. We report on a method for system-level test case generation. This method relies on dynamic interleaving of scenarios from the core level or sub-system level. We discuss the relevance of this method for the system level. We also describe a tool that implements this method and show how it was used in IBM for system verification of the Xbox 360 chip and power management in the cell processor, as well as verification of the pSeries eServers. We claim that this method shortened the system level verification cycle and allowed reuse in and across projects, which led to exposure of system-level bugs in a relatively short time.

Parallelism in processor architecture and design imposes a verification challenge as the exponential growth in the number of execution combinations becomes unwieldy. In this paper we report on the verification of a Very Large Instruction Word processor. The verification team used a sophisticated test program generator that modeled the parallel aspects as sequential constraints, and augmented the tool with manually written test templates. The system created large numbers of legal stimuli, however the quality of the tests was proved insufficient by several post silicon bugs. We analyze this experience and suggest an alternative, parallel generation technique. We show through experiments the feasibility of the new technique and its superior quality along several dimensions. We claim that the results apply to other parallel architectures and verification environments.

We report on our experience with a new test generation language for processor verification. The verification of two superscalar multiprocessors is described and we show the ease of expressing complex verification tasks. The cost and benefit are demonstrated: training takes up to six months; the simulation time required for a desired level of coverage has decreased by a factor of twenty; the number of escape bugs has been reduced.