The calibration of modern internal combustion engines requires ever improving measurement data quality such that they comply with increasingly stringent emissions legislation. This study establishes methodology and a software tool to improve the quality of steady-state emissions measurements from engine dynamometer tests. Literature shows state of the art instrumentation are necessary to monitor the cycle-by-cycle variations that significantly alter emissions measurements. Test methodologies that consider emissions formation mechanisms invariably focus on thermal transients and preconditioning of internal surfaces. This work sought data quality improvements using three principle approaches. An adapted steady-state identifier to more reliably indicate when the test conditions reached steady-state; engine preconditioning to reduce the influence of the prior day’s operating conditions on the measurements; and test point ordering to reduce measurement deviation. Selection of an improved steady-state indicator was identified using correlations in test data. It was shown by repeating forty steady-state test points that a more robust steady-state indicator has the potential to reduce the measurement deviation of particulate number by 6%, unburned hydrocarbons by 24%, carbon monoxide by 10% and oxides of nitrogen by 29%. The variation of emissions measurements from those normally observed at a repeat baseline test point were significantly influenced by varying the preconditioning power. Preconditioning at the baseline operating condition converged emissions measurements with the mean of those typically observed. Changing the sequence of steady-state test points caused significant differences in the measured engine performance. Examining the causes of measurement deviation allowed an optimised test point sequencing method to be developed. A 30% reduction in measurement deviation of a targeted engine response (particulate number emissions) was obtained using the developed test methodology. This was achieved by selecting an appropriate steady-state indicator and sequencing test points. The benefits of preconditioning were deemed short-lived and impractical to apply in every-day engine testing although the principles were considered when developing the sequencing methodology.

With the advancement of the Internet and related technologies, many traditional crimes have made the leap to digital environments. The successes of data mining in a wide variety of disciplines have given birth to crime analysis. Traditional crime analysis is mainly focused on understanding crime patterns, however, it is unsuitable for identifying and monitoring emerging crimes. The true nature of crime remains buried in unstructured content that represents the hidden story behind the data. User feedback leaves valuable traces that can be utilised to measure the quality of various aspects of products or services and can also be used to detect, infer, or predict crimes. Like any application of data mining, the data must be of a high quality standard in order to avoid erroneous conclusions. This thesis presents a methodology and practical experiments towards discovering whether (i) user feedback can be harnessed and processed for crime intelligence, (ii) criminal associations, structures, and roles can be inferred among entities involved in a crime, and (iii) methods and standards can be developed for measuring, predicting, and comparing the quality level of social data instances and samples. It contributes to the theory, design and development of a novel framework for crime intelligence and algorithm for the estimation of social data quality by innovatively adapting the methods of monitoring water contaminants. Several experiments were conducted and the results obtained revealed the significance of this study in mining social data for crime intelligence and in developing social data quality filters and decision support systems. / Commonwealth Scholarship Commission.

In organisations, duplicate customer master data present a recurring problem. Duplicate records can result in errors, complication, and inefficiency since they frequently result from dissimilar systems or inadequate data integration. Since this problem is made more complicated by changing client information over time, prompt detection and correction are essential. In addition to improving data quality, eliminating duplicate information also improves business processes, boosts customer confidence, and makes it easier to make wise decisions. This master’s thesis explores machine learning’s application to the field of Master Data Management. The main objective of the project is to assess how machine learning may improve the accuracy and consistency of master data records. The project aims to support the improvement of data quality within enterprises by managing issues like duplicate customer data. One of the research topics of study is if machine learning can be used to improve the accuracy of customer data, and another is whether it can be used to investigate scientific models for customer analysis when cleaning data using machine learning. Dimension identification, appropriate algorithm selection, appropriate parameter value selection, and output analysis are the four steps in the study's process. As a ground truth for our project, we came to conclusion that 22,000 is the correct number of clusters for our clustering algorithms which represents the number of unique customers. Saying this, the best performing algorithm based on number of clusters and the silhouette score metric turned out the be KMEANS with 22,000 clusters and a silhouette score of 0.596, followed by BIRCH with 22,000 number of clusters and a silhouette score of 0.591.

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No description available.

<p>Extensive assemblages of glacial erosional features are commonly observed on bedrock outcrops in deglaciated landscapes. There is considerable debate regarding the origins of many subglacial erosional landforms, due to a relative paucity of detailed data concerning these features and a need for improved understanding of the subglacial processes that may form them. This study presents detailed documentation and maps of assemblages of glacial erosional features from select field sites throughout the Great Lakes basins. The characteristics and spatial distribution of p-forms exposed on variable substrates at the Whitefish Falls, Vineland, Pelee Island and Kelleys Island field sites were investigated in order to determine the mode of p-form origin to identify significant spatial and temporal variability in subglacial processes operating at these locations. Observations from this work suggest that p-forms evolve through multiple phases of erosion, whereby glacial ice initially abrades the bedrock surface, leaving behind streamlined bedrock highs, striations and glacial grooves. Subsequent erosion by vortices in turbulent subglacial meltwater sculpts the flanks of bedrock highs and grooves into p-forms. These forms are subjected to a second phase of subglacial abrasion that ornaments the sinuous, sharp rimmed features with linear striae. The presence of multi-directional (‘chaotic’) striae at some sites suggests erosion by saturated till may contribute to, but is not essential for p-form development. Investigation in the Halton Hills region of Ontario focused on modeling bedrock topography in order to delineate the extent and geometry of buried bedrock valleys thought to host potential municipal significant aquifer units. Various approaches to subsurface modeling were investigated in the Halton Hills region using a combination of primary data (collected from boreholes and outcrop), intermediate data collected through aerial photography and consultant reports, and extensively screened low quality data from the Ontario Waterwell Database. A new, ‘quality weighted’ approach to modeling variable quality data was explored but proved ineffective for the purposes of this study, as differential weighting of high and low quality data either over-smoothed the model or significantly altered data values. A series of models were interpolated and compared using calculated RMSE values derived from model cross-validation. The preferred bedrock topography model of the Halton Hills region had the lowest RMSE score, and allowed identification of three major buried bedrock valleys systems (the Georgetown, Acton and 16 Mile Creek buried valleys) which contain up to 40 – 50 m of Quaternary infill. These valleys were likely carved through a combination of fluvial and glacial erosion during the late Quaternary period, and their orientation may be influenced by pre-existing structural weaknesses in the bedrock. Future work on subglacial erosional landforms should focus on the temporal scale in which subglacial processes, through association with other subglacial landforms and dating methods.</p> / Master of Science (MSc)

No / Smart Sustainable Cities (SSC) consist of multiple stakeholders, who must cooperate in order for SSCs to be successful. Housing is an important challenge and in many cities, therefore, a key stakeholder are social housing organisations. This paper introduces a qualitative case study of a social housing provider in the UK who implemented a business intelligence project (a method to assess data networks within an organisation) to increase data quality and data interoperability. Our analysis suggests that creating pathways for different information systems within an organisation to ‘talk to’ each other is the first step. Some of the issues during the project implementation include the lack of training and development, organisational reluctance to change, and the lack of a project plan. The challenges faced by the organisation during this project can be helpful for those implementing SSCs. Currently, many SSC frameworks and models exist, yet most seem to neglect localised challenges faced by the different stak

Yes / The reuse of existing datasets to identify mechanisms for improving healthcare quality has been widely encouraged. There has been limited application within dementia care. Dementia Care Mapping (DCM) is an observational tool in widespread use, predominantly to assess and improve quality of care in single organisations. DCM data has the potential to be used for secondary purposes to improve quality of care. However, its suitability for such use requires careful evaluation. This study conducted in-depth interviews with 29 DCM users to identify issues, concerns and challenges regarding the secondary use of DCM data. Data was analysed using modified Grounded Theory. Major themes identified included the need to collect complimentary contextual data in addition to DCM data, to reassure users regarding ethical issues associated with storage and reuse of care related data and the need to assess and specify data quality for any data that might be available for secondary analysis. / This study was funded by the Faculty of Health Studies, University of Bradford.

Yes / Secondary use of data via integrated health information technology is fundamental to many healthcare policies and processes worldwide. However, repurposing data can be problematic and little research has been undertaken into the everyday practicalities of inter-system data sharing that helps explain why this is so, especially within (as opposed to between) organisations. In response, this article reports one of the most detailed empirical examinations undertaken to date of the work involved in repurposing healthcare data for National Clinical Audits. Methods: Fifty-four semi-structured, qualitative interviews were carried out with staff in five English National Health Service hospitals about their audit work, including 20 staff involved substantively with audit data collection. In addition, ethnographic observations took place on wards, in ‘back offices’ and meetings (102 hours). Findings were analysed thematically and synthesised in narratives. Results: Although data were available within hospital applications for secondary use in some audit fields, which could, in theory, have been auto-populated, in practice staff regularly negotiated multiple, unintegrated systems to generate audit records. This work was complex and skilful, and involved cross-checking and double data entry, often using paper forms, to assure data quality and inform quality improvements. Conclusions: If technology is to facilitate the secondary use of healthcare data, the skilled but largely hidden labour of those who collect and recontextualise those data must be recognised. Their detailed understandings of what it takes to produce high quality data in specific contexts should inform the further development of integrated systems within organisations.

This research determines whether data quality mining can be used to describe, monitor and evaluate the scope and impact of semantic data quality problems in the learner enrolment data on the National Learners’ Records Database. Previous data quality mining work has focused on anomaly detection and has assumed that the data quality aspect being measured exists as a data value in the data set being mined. The method for this research is quantitative in that the data mining techniques and model that are best suited for semantic data quality deficiencies are identified and then applied to the data. The research determines that unsupervised data mining techniques that allow for weighted analysis of the data would be most suitable for the data mining of semantic data deficiencies. Further, the academic Knowledge Discovery in Databases model needs to be amended when applied to data mining semantic data quality deficiencies. / School of Computing / M. Tech. (Information Technology)