A survey on cost-effective context-aware distribution of social data streams over energy-efficient data centres

Social media have emerged in the last decade as a viable and ubiquitous means of communication. The ease of user content generation within these platforms, e.g. check-in information, multimedia data, etc., along with the proliferation of Global Positioning System (GPS)-enabled, always-connected capture devices lead to data streams of unprecedented amount and a radical change in information sharing. Social data streams raise a variety of practical challenges, including derivation of real-time meaningful insights from effectively gathered social information, as well as a paradigm shift for content distribution with the leverage of contextual data associated with user preferences, geographical characteristics and devices in general. In this article we present a comprehensive survey that outlines the state-of-the-art situation and organizes challenges concerning social media streams and the infrastructure of the data centres supporting the efficient access to data streams in terms of content distribution, data diffusion, data replication, energy efficiency and network infrastructure. We systematize the existing literature and proceed to identify and analyse the main research points and industrial efforts in the area as far as modelling, simulation and performance evaluation are concerned

Contextual Analysis of Large-Scale Biomedical Associations for the Elucidation and Prioritization of Genes and their Roles in Complex Disease

Vast amounts of biomedical associations are easily accessible in public resources, spanning gene-disease associations, tissue-specific gene expression, gene function and pathway annotations, and many other data types. Despite this mass of data, information most relevant to the study of a particular disease remains loosely coupled and difficult to incorporate into ongoing research. Current public databases are difficult to navigate and do not interoperate well due to the plethora of interfaces and varying biomedical concept identifiers used. Because no coherent display of data within a specific problem domain is available, finding the latent relationships associated with a disease of interest is impractical. This research describes a method for extracting the contextual relationships embedded within associations relevant to a disease of interest. After applying the method to a small test data set, a large-scale integrated association network is constructed for application of a network propagation technique that helps uncover more distant latent relationships. Together these methods are adept at uncovering highly relevant relationships without any a priori knowledge of the disease of interest. The combined contextual search and relevance methods power a tool which makes pertinent biomedical associations easier to find, easier to assimilate into ongoing work, and more prominent than currently available databases. Increasing the accessibility of current information is an important component to understanding high-throughput experimental results and surviving the data deluge

Designing Resilient Manufacturing Systems In the Presence of Change

Economic and technical changes force manufacturers to redesign and enhance their operational systems. The implications of such changes within a complex system such as manufacturing and the supply chain can be very challenging. In particular, where the number of system elements and their connections result in a high level of complexity, the potential effects of a change can be expensive concerning the delivery time and cost targets, as a change to one part or element of a design requires additional changes throughout the system. Companies need to understand the characteristics of their manufacturing systems that make them resilient to change. Considered from a system perspective, the structures of the system, and its elements and connections, contribute greatly to the characteristics and behavior of the system and hence potential resilience. A change prediction method can help to analyse the change properties and improve complex systems by focusing on the underlying structural elements and dependencies. This thesis proposes a novel system change method that can enable the review of the current manufacturing system and understand how to design a more robust or adaptable system that addresses resilience. This method is a combination of matrix-based approaches and methods to assess the interaction between elements of the product and its manufacturing process in order to understand the risk of changes propagating through the system. Risk assessment across layers of a system can give valuable insight into how an element change interacts within the system. The goal of this thesis is to contribute to gaining a fundamental understanding of manufacturing systems resilience by developing a method to evaluate capabilities of changes, performance robustness or adaptability, and achieving high resilience.Universal Oil Products (a Honeywell Company); Laing O’Rourk

Novel processes for smart grid information exchange and knowledge representation using the IEC common information model

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The IEC Common Information Model (CIM) is of central importance in enabling smart grid interoperability. Its continual development aims to meet the needs of the smart grid for semantic understanding and knowledge representation for a widening domain of resources and processes. With smart grid evolution the importance of information and data management has become an increasingly pressing issue not only because far more data is being generated using modern sensing, control and measuring devices but also because information is now becoming recognised as the ‘integral component’ that facilitates the optimal flexibility required of the smart grid. This thesis looks at the impacts of CIM implementation upon the landscape of smart grid issues and presents research from within National Grid contributing to three key areas in support of further CIM deployment. Taking the issue of Enterprise Information Management first, an information management framework is presented for CIM deployment at National Grid. Following this the development and demonstration of a novel secure cloud computing platform to handle such information is described. Power system application (PSA) models of the grid are partial knowledge representations of a shared reality. To develop the completeness of our understanding of this reality it is necessary to combine these representations. The second research contribution reports on a novel methodology for a CIM-based model repository to align PSA representations and provide a knowledge resource for building utility business intelligence of the grid. The third contribution addresses the need for greater integration of information relating to energy storage, an essential aspect of smart energy management. It presents the strategic rationale for integrated energy modeling and a novel extension to the existing CIM standards for modeling grid-scale energy storage. Significantly, this work has already contributed to a larger body of work on modeling Distributed Energy Resources currently under development at the Electric Power Research Institute (EPRI) in the USA.Dr. Martin Bradley on behalf of National Grid Plc. and the Engineering and Physical Sciences Research Council (EPSRC

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Enabling the human in the loop: Linked data and knowledge in industrial cyber-physical systems

Industrial Cyber-Physical Systems have benefitted substantially from the introduction of a range of technology enablers. These include web-based and semantic computing, ubiquitous sensing, internet of things (IoT) with multi-connectivity, advanced computing architectures and digital platforms, coupled with edge or cloud side data management and analytics, and have contributed to shaping up enhanced or new data value chains in manufacturing. While parts of such data flows are increasingly automated, there is now a greater demand for more effectively integrating, rather than eliminating, human cognitive capabilities in the loop of production related processes. Human integration in Cyber-Physical environments can already be digitally supported in various ways. However, incorporating human skills and tangible knowledge requires approaches and technological solutions that facilitate the engagement of personnel within technical systems in ways that take advantage or amplify their cognitive capabilities to achieve more effective sociotechnical systems. After analysing related research, this paper introduces a novel viewpoint for enabling human in the loop engagement linked to cognitive capabilities and highlighting the role of context information management in industrial systems. Furthermore, it presents examples of technology enablers for placing the human in the loop at selected application cases relevant to production environments. Such placement benefits from the joint management of linked maintenance data and knowledge, expands the power of machine learning for asset awareness with embedded event detection, and facilitates IoT-driven analytics for product lifecycle management

Fusion of Information and Analytics: A Discussion on Potential Methods to Cope with Uncertainty in Complex Environments (Big Data and IoT)

International audienceInformation overload and complexity are core problems to most organizations of today. The advances in networking capabilities have created the conditions of complexity by enabling richer, real-time interactions between and among individuals, objects, systems and organizations. Fusion of Information and Analytics Technologies (FIAT) are key enablers for the design of current and future decision support systems to support prognosis, diagnosis, and prescriptive tasks in such complex environments. Hundreds of methods and technologies exist, and several books have been dedicated to either analytics or information fusion so far. However, very few have discussed the methodological aspects and the need of integrating frameworks for these techniques coming from multiple disciplines. This paper presents a discussion of potential integrating frameworks as well as the development of a computational model to evolve FIAT-based systems capable of meeting the challenges of complex environments such as in Big Data and Internet of Things (IoT)