First and foremost, I would like to thank my supervisors, David S. Rosenblum and Anthony Finkelstein, for their support during the past three years. I am indebted to David for his help and patience; for providing me the freedom to pursue interesting research; and for considering with me over long Skype calls the finer points of OWL-LP integration in the context of domain-specific and probabilistic model checking. Anthony's advice on authoring compelling narratives that clarify complex research, and his encouragement and strategic guidance, have been invaluable for my development as a researcher. I have been fortunate to visit the Nebraska Intelligent MoBile Unmanned Systems (NIMBUS) Lab at the University of Nebraska-Lincoln (UNL). I would like to thank Sebastian Elbaum, co-founder of NIMBUS, for working with me to focus the research in this thesis, and for his contribution throughout my PhD. I would also like to thank Emmanuel Letier for his insightful feedback during my first and second year vivas. And I would like to thank faculty and staff at the Department of Computer Science at University College London (UCL) for providing me with the resources to develop this thesis. In particular, I would like to thank Dean Mohamedally,

Abstract. This paper presents a proposed approach to address risk and regulation management within the highly active and volatile financial domain by employing semantic based technologies within a collaborative networks environment. Firstly the problems and motivation are introduced, with accent on big data and high frequency trading issues that are creating major problems to the current software systems. Secondly the state of the art on Big Data, Regulation and Risk Management are presented. Next the FIORD platform architecture is detailed and the envisioned approach explained. Finally conclusions are presented where benefits for real time monitoring are emphasized so high frequency trading irregularities are detected in real time for the benefit of involved financial institutions.

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