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

Inclusive education policy, the general allocation model and dilemmas of practice in primary schools

Background: Inclusive education is central to contemporary discourse internationally reflecting societies’ wider commitment to social inclusion. Education has witnessed transforming approaches that have created differing distributions of power, resource allocation and accountability. Multiple actors are being forced to consider changes to how key services and supports are organised. This research constitutes a case study situated within this broader social service dilemma of how to distribute finite resources equitably to meet individual need, while advancing inclusion. It focuses on the national directive with regard to inclusive educational practice for primary schools, Department of Education and Science Special Education Circular 02/05, which introduced the General Allocation Model (GAM) within the legislative context of the Education of Persons with Special Educational Needs (EPSEN) Act (Government of Ireland, 2004). This research could help to inform policy with ‘facts about what is happening on the ground’ (Quinn, 2013). Research Aims: The research set out to unearth the assumptions and definitions embedded within the policy document, to analyse how those who are at the coalface of policy, and who interface with multiple interests in primary schools, understand the GAM and respond to it, and to investigate its effects on students and their education. It examines student outcomes in the primary schools where the GAM was investigated. Methods and Sample The post-structural study acknowledges the importance of policy analysis which explicitly links the ‘bigger worlds’ of global and national policy contexts to the ‘smaller worlds’ of policies and practices within schools and classrooms. This study insists upon taking the detail seriously (Ozga, 1990). A mixed methods approach to data collection and analysis is applied. In order to secure the perspectives of key stakeholders, semi-structured interviews were conducted with primary school principals, class teachers and learning support/resource teachers (n=14) in three distinct mainstream, non-DEIS schools. Data from the schools and their environs provided a profile of students. The researcher then used the Pobal Maps Facility (available at www.pobal.ie) to identify the Small Area (SA) in which each student resides, and to assign values to each address based on the Pobal HP Deprivation Index (Haase and Pratschke, 2012). Analysis of the datasets, guided by the conceptual framework of the policy cycle (Ball, 1994), revealed a number of significant themes. Results: Data illustrate that the main model to support student need is withdrawal from the classroom under policy that espouses inclusion. Quantitative data, in particular, highlighted an association between segregated practice and lower socioeconomic status (LSES) backgrounds of students. Up to 83% of the students in special education programmes are from lower socio-economic status (LSES) backgrounds. In some schools 94% of students from LSES backgrounds are withdrawn from classrooms daily for special education. While the internal processes of schooling are not solely to blame for class inequalities, this study reveals the power of professionals to order children in school, which has implications for segregated special education practice. Such agency on the part of key actors in the context of practice relates to ‘local constructions of dis/ability’, which is influenced by teacher habitus (Bourdieu, 1984). The researcher contends that inclusive education has not resulted in positive outcomes for students from LSES backgrounds because it is built on faulty assumptions that focus on a psycho-medical perspective of dis/ability, that is, placement decisions do not consider the intersectionality of dis/ability with class or culture. This study argues that the student need for support is better understood as ‘home/school discontinuity’ not ‘disability’. Moreover, the study unearths the power of some parents to use social and cultural capital to ensure eligibility to enhanced resources. Therefore, a hierarchical system has developed in mainstream schools as a result of funding models to support need in inclusive settings. Furthermore, all schools in the study are ‘ordinary’ schools yet participants acknowledged that some schools are more ‘advantaged’, which may suggest that ‘ordinary’ schools serve to ‘bury class’ (Reay, 2010) as a key marker in allocating resources. The research suggests that general allocation models of funding to meet the needs of students demands a systematic approach grounded in reallocating funds from where they have less benefit to where they have more. The calculation of the composite Haase Value in respect of the student cohort in receipt of special education support adopted for this study could be usefully applied at a national level to ensure that the greatest level of support is targeted at greatest need. Conclusion: In summary, the study reveals that existing structures constrain and enable agents, whose interactions produce intended and unintended consequences. The study suggests that policy should be viewed as a continuous and evolving cycle (Ball, 1994) where actors in each of the social contexts have a shared responsibility in the evolution of education that is equitable, excellent and inclusive.

Monitoring and modelling the morphodynamic evolution of a breached barrier beach system

Predicting the evolution of a coastal cell requires the identification of the key drivers of morphology. Soft coastlines are naturally dynamic but severe storm events and even human intervention can accelerate any changes that are occurring. However, when erosive events such as barrier breaching occur with no obvious contributory factors, a deeper understanding of the underlying coastal processes is required. Ideally conclusions on morphological drivers should be drawn from field data collection and remote sensing over a long period of time. Unfortunately, when the Rossbeigh barrier beach in Dingle Bay, County Kerry, began to erode rapidly in the early 2000’s, eventually leading to it breaching in 2008, no such baseline data existed. This thesis presents a study of the morphodynamic evolution of the Inner Dingle Bay coastal system. The study combines existing coastal zone analysis approaches with experimental field data collection techniques and a novel approach to long term morphodynamic modelling to predict the evolution of the barrier beach inlet system. A conceptual model describing the long term evolution of Inner Dingle Bay in 5 stages post breaching was developed. The dominant coastal processes driving the evolution of the coastal system were identified and quantified. A new methodology of long term process based numerical modelling approach to coastal evolution was developed. This method was used to predict over 20 years of coastal evolution in Inner Dingle Bay. On a broader context this thesis utilised several experimental coastal zone data collection and analysis methods such as ocean radar and grain size trend analysis. These were applied during the study and their suitability to a dynamic coastal system was assessed.

Time-limited home-care reablement services for maintaining and improving the functional independence of older adults

Background: Reablement, also known as restorative care, is one possible approach to home-care services for older adults at risk of functional decline. Unlike traditional home-care services, reablement is frequently time-limited (usually six to 12 weeks) and aims to maximise independence by offering an intensive multidisciplinary, person-centred and goal-directed intervention. Objectives: To assess the effects of time-limited home-care reablement services (up to 12 weeks) for maintaining and improving the functional independence of older adults (aged 65 years or more) when compared to usual home-care or wait-list control group. Search methods: We searched the following databases with no language restrictions during April to June 2015: the Cochrane Central Register of Controlled Trials (CENTRAL); MEDLINE (OvidSP); Embase (OvidSP); PsycINFO (OvidSP); ERIC; Sociological Abstracts; ProQuest Dissertations and Theses; CINAHL (EBSCOhost); SIGLE (OpenGrey); AgeLine and Social Care Online. We also searched the reference lists of relevant studies and reviews as well as contacting authors in the field. Selection criteria: We included randomised controlled trials (RCTs), cluster randomised or quasi-randomised trials of time-limited reablement services for older adults (aged 65 years or more) delivered in their home; and incorporated a usual home-care or wait-list control group. Data collection and analysis: Two authors independently assessed studies for inclusion, extracted data, assessed the risk of bias of individual studies and considered quality of the evidence using GRADE. We contacted study authors for additional information where needed. Main results: Two studies, comparing reablement with usual home-care services with 811 participants, met our eligibility criteria for inclusion; we also identified three potentially eligible studies, but findings were not yet available. One included study was conducted in Western Australia with 750 participants (mean age 82.29 years). The second study was conducted in Norway (61 participants; mean age 79 years). We are very uncertain as to the effects of reablement compared with usual care as the evidence was of very low quality for all of the outcomes reported. The main findings were as follows. Functional status: very low quality evidence suggested that reablement may be slightly more effective than usual care in improving function at nine to 12 months (lower scores reflect greater independence; standardised mean difference (SMD) -0.30; 95% confidence interval (CI) -0.53 to -0.06; 2 studies with 249 participants). Adverse events: reablement may make little or no difference to mortality at 12 months' follow-up (RR 0.97; 95% CI 0.74 to 1.29; 2 studies with 811 participants) or rates of unplanned hospital admission at 24 months (RR 0.94; 95% CI 0.85 to 1.03; 1 study with 750 participants). The very low quality evidence also means we are uncertain whether reablement may influence quality of life (SMD -0.23; 95% CI -0.48 to 0.02; 2 trials with 249 participants) or living arrangements (RR 0.92, 95% CI 0.62 to 1.34; 1 study with 750 participants) at time points up to 12 months. People receiving reablement may be slightly less likely to have been approved for a higher level of personal care than people receiving usual care over the 24 months' follow-up (RR 0.87; 95% CI 0.77 to 0.98; 1 trial, 750 participants). Similarly, although there may be a small reduction in total aggregated home and healthcare costs over the 24-month follow-up (reablement: AUD 19,888; usual care: AUD 22,757; 1 trial with 750 participants), we are uncertain about the size and importance of these effects as the results were based on very low quality evidence. Neither study reported user satisfaction with the service. Authors' conclusions: There is considerable uncertainty regarding the effects of reablement as the evidence was of very low quality according to our GRADE ratings. Therefore, the effectiveness of reablement services cannot be supported or refuted until more robust evidence becomes available. There is an urgent need for high quality trials across different health and social care systems due to the increasingly high profile of reablement services in policy and practice in several countries.