A distributed architecture for the monitoring and analysis of time series data

It is estimated that the quantity of digital data being transferred, processed or stored at any one time currently stands at 4.4 zettabytes (4.4 × 2 70 bytes) and this figure is expected to have grown by a factor of 10 to 44 zettabytes by 2020. Exploiting this data is, and will remain, a significant challenge. At present there is the capacity to store 33% of digital data in existence at any one time; by 2020 this capacity is expected to fall to 15%. These statistics suggest that, in the era of Big Data, the identification of important, exploitable data will need to be done in a timely manner. Systems for the monitoring and analysis of data, e.g. stock markets, smart grids and sensor networks, can be made up of massive numbers of individual components. These components can be geographically distributed yet may interact with one another via continuous data streams, which in turn may affect the state of the sender or receiver. This introduces a dynamic causality, which further complicates the overall system by introducing a temporal constraint that is difficult to accommodate. Practical approaches to realising the system described above have led to a multiplicity of analysis techniques, each of which concentrates on specific characteristics of the system being analysed and treats these characteristics as the dominant component affecting the results being sought. The multiplicity of analysis techniques introduces another layer of heterogeneity, that is heterogeneity of approach, partitioning the field to the extent that results from one domain are difficult to exploit in another. The question is asked can a generic solution for the monitoring and analysis of data that: accommodates temporal constraints; bridges the gap between expert knowledge and raw data; and enables data to be effectively interpreted and exploited in a transparent manner, be identified? The approach proposed in this dissertation acquires, analyses and processes data in a manner that is free of the constraints of any particular analysis technique, while at the same time facilitating these techniques where appropriate. Constraints are applied by defining a workflow based on the production, interpretation and consumption of data. This supports the application of different analysis techniques on the same raw data without the danger of incorporating hidden bias that may exist. To illustrate and to realise this approach a software platform has been created that allows for the transparent analysis of data, combining analysis techniques with a maintainable record of provenance so that independent third party analysis can be applied to verify any derived conclusions. In order to demonstrate these concepts, a complex real world example involving the near real-time capturing and analysis of neurophysiological data from a neonatal intensive care unit (NICU) was chosen. A system was engineered to gather raw data, analyse that data using different analysis techniques, uncover information, incorporate that information into the system and curate the evolution of the discovered knowledge. The application domain was chosen for three reasons: firstly because it is complex and no comprehensive solution exists; secondly, it requires tight interaction with domain experts, thus requiring the handling of subjective knowledge and inference; and thirdly, given the dearth of neurophysiologists, there is a real world need to provide a solution for this domain

Studies of ferrite, borosilicate and intercalation materials

This thesis is concerned with several aspects of the chemistry of iron compounds. The preparation (with particular emphasis on coprecipitation and sol-gel techniques) and processing of ferrites are discussed. Chapter 2 describes the synthesis of Ni-Zn ferrites with various compositions by three methods. These methods include coprecipitation and sol-gel techniques. The Ni-Zn ferrites were characterised by powder X-ray diffactometry (PXRD), scanning electron microscopy (SEM), vibrating sample magnetometry (VSM), Mössbauer spectroscopy and resistivity measurements. The results for the corresponding ferrites prepared by each method are compared. Chapter 3 reports the sol-gel preparation of a lead borosilicate glass and its addition to Ni-Zn ferrites prepared by the sol-gel method in Chapter 2. The glass-ferrites formed were analysed by the same techniques employed in Chapter 2. Alterations in the microstructure, magnetic and electronic properties of the ferrites due to glass addition are described. Chapter 4 introduces compounds containing Fe-O-B, Fe-O-Si or B-O-Si linkages. The synthesis and characterisation of compounds containing Fe-O-B units are described. The structure of [Fe(SALEN)]2O.CH2Cl2 (17), used in attempts to prepare compounds with Fe-O-Si bonds, was determined by X-ray crystallography. Chapter 4 also details the synthesis of three new borosilicate compounds containing ferrocenyl groups, i.e. [FcBO)2(OSiBut2)2] (19), [(FcBO)2(OSiPh2)2] (20) and [FcBOSiPh3] (21). The structure of (19) was determined by X-ray Crystallographic analysis. Chapter 5 reviews the intercalation properties of the layered host compound iron oxychloride (FeOCI). Intercalation compounds prepared with the microwave dielectric heating technique are also discussed. The syntheses of intercalation compounds by the microwave method with FeOCI as host and ferrocene, ferrocenylboronic acid and 4-aminopyridine as guest species are described. Characterisation of these compounds by powder X-ray diffractometry (PXRD) and M{ssbauer spectroscopy is reported. The attempted synthesis of an intercalation compound with the borosilicate compound (19) as guest species is discussed. Appendices A-E describe the theory and instrumentation involved in powder X-ray diffractometry (PXRD), scanning electron microscopy (SEM0, vibrating sample magnetometry (VSM), Mössbauer spectroscopy and electrical resistivity measurements, respectively. Appendix F details the attempted syntheses of compounds with Fe-O-B and Fe-O-Si linkages.

Uniformly convergent finite element methods for singularly perturbed parabolic partial differential equations

This thesis is concerned with uniformly convergent finite element methods for numerically solving singularly perturbed parabolic partial differential equations in one space variable. First, we use Petrov-Galerkin finite element methods to generate three schemes for such problems, each of these schemes uses exponentially fitted elements in space. Two of them are lumped and the other is non-lumped. On meshes which are either arbitrary or slightly restricted, we derive global energy norm and L2 norm error bounds, uniformly in the diffusion parameter. Under some reasonable global assumptions together with realistic local assumptions on the solution and its derivatives, we prove that these exponentially fitted schemes are locally uniformly convergent, with order one, in a discrete L∞norm both outside and inside the boundary layer. We next analyse a streamline diffusion scheme on a Shishkin mesh for a model singularly perturbed parabolic partial differential equation. The method with piecewise linear space-time elements is shown, under reasonable assumptions on the solution, to be convergent, independently of the diffusion parameter, with a pointwise accuracy of almost order 5/4 outside layers and almost order 3/4 inside the boundary layer. Numerical results for the above schemes are presented. Finally, we examine a cell vertex finite volume method which is applied to a model time-dependent convection-diffusion problem. Local errors away from all layers are obtained in the l2 seminorm by using techniques from finite element analysis.