The long-term evolution of energy systems is set by the investment decisions of very many actors such as up-stream resource companies, power plant operators, network infrastructure providers, vehicle owners, transport system operators and building developers and occupiers. But these decisions are deliberately shaped by markets and incentives that have been designed by local and national governments to achieve policy objectives on energy, air-quality, economic growth and so on. It is clear then that government and businesses need detailed and dependable evidence of what can be achieved, what format of energy system we should aim for, what new technologies need to be encouraged, and how energy systems can form part of an industrial strategy to new goods and services. It is widely accepted that a whole-system view of energy is needed, covering not only multiple energy sectors (gas, heat, electricity and transport fuel) but also the behaviour of individuals and organisations within the energy consuming sectors such as transport and the built environment. This means that modelling energy production, delivery and use in a future integrated system is highly complex and analytically challenging. To provide evidence to government and business on what an optimised future system may look like, one has to rise to these modelling challenges. For electricity systems alone, there are established models that can optimise for security, cost and emissions given some assumptions (and sensitivities) and these have been used to provide policy and business strategy evidence. However, such models do not exist for the complex interactions of integrated systems and not at the level of fine detailed needed to expose particularly difficult operating conditions. Our vision is to tackle the very challenging modelling required for integrated energy systems by combining multi-physics optimising techno-economic models with machine learning of human behaviour and operational models emerging multi-carrier network and conversion technologies. The direction we wish to take is clear but there are many detailed challenges along the way for which highly innovative solutions will be needed to overcome the hurdles encountered. The programme grant structure enables us to assemble an exceptional team of experts across many disciplines. There are new and exciting opportunities, for instance, to apply machine learning to identify in a quantitative way models of consumer behaviour and responsiveness to incentives that can help explore demand-side flexibility within an integrated energy system. We have engaged four major partners from complementary sectors of the energy system that will support the programme with significant funding (approximately 35% additional funding) and more importantly engage with us and each other to share insights, challenges, data and case studies. EDF Energy provide the perspective on an energy retail business and access to smart meter trail data. Shell provide insights into the future fuels to be used in transport and building services. National Grid (System Operator) give the perspective of the use of flexibility and new service propositions for efficient system operations. ABB are a provider of data acquisition and control systems and provide industrial perspective of decentralisation of control. ABB have committed to providing substantial equipment and resource to build a verification and demonstration facility for decentralised control. We are also engaging examples of the new entrants, often smaller companies with potentially disruptive technologies and business models, who will engage and share some of their insights.

Advanced applications relying on intelligent management of loosely structured and large-scale datasets play a key role in domains such as healthcare, business and government. Ontology-based technologies lie at the core of many such applications. In a nutshell, an ontology-based data management system (ODMS) enables intelligent information processing by providing means for representing background knowledge about the application in an ontology, and exploiting automated reasoning techniques to infer information that is implicit in the data and the ontology. State-of-the art ODMSs are, however, not well-suited for applications which require real-time analysis of rapidly changing data. For instance, oil and gas companies continuously monitor sensor readings to detect equipment malfunction and predict maintenance needs; network providers analyse flow data to identify traffic anomalies and Denial of Service attacks; knowledge graphs are continuously updated; and Internet of Things (IoT) applications such as Smart Cities require real-time analysis of data stemming from multiple types of device. ODMSs often borrow implementation techniques from the database literature, where real-time analysis of rapidly changing data has been tackled using two main approaches. (1) In a stream processing system, the input data is conceptually seen as an unbounded sequence of time-stamped tuples that flow through the system; data is only available for processing in a single pass and information stored by the system is inherently incomplete. Streaming jobs are long-running: queries are deployed once and continue to produce results until removed.State-of-the art systems, such as Apache Storm, Apache Spark Streaming, Google's Millwheel, Linked In's Samza, and Apache Flink, achieve sub-second latencies by distributing the streaming workload in a cluster, which requires sophisticated scheduling and fault-tolerance techniques. (2) In a real-time database, the data is seen as a finite collection of records that is continuously evolving. This traditional concept of a finite and persistent collection is ubiquitous in the database world is well-suited for applications requiring a consistent and complete view of the data.The key feature that distinguishes real-time from traditional databases is that, similarly to streaming systems, they allow clients to subscribe to long-running continuous queries that instantaneously push incremental updates. Many theoretical and practical difficulties arise, however, when adapting these approaches to ODMSs. In the OASIS project, we will address these difficulties and lay the foundations for a new generation of ODMSs capable of ingesting and processing rapidly changing data in real time. Such systems will support the aforementioned applications by enabling fast execution of complex analytics pipelines supporting intelligent decisions. Moreover, we will exploit the resulting insights to implement a prototype and test it in real-life deployments.