Dissertations / Theses on the topic 'Data modelling frameworks'
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Bryan-Kinns, Nicholas Jonathan. "A framework for modelling video content." Thesis, Queen Mary, University of London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287876.
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Hempel, Arne-Jens, and Steffen F. Bocklisch. "Parametric Fuzzy Modelling Framework for Complex Data-Inherent Structures." Universitätsbibliothek Chemnitz, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200901487.
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The present article dedicates itself to fuzzy modelling
of data-inherent structures. In particular two main points are dealt
with: the introduction of a fuzzy modelling framework and the elaboration
of an automated, data-driven design strategy to model complex
data-inherent structures within this framework.
The innovation concerning the modelling framework lies in the
fact that it is consistently built around a single, generic type of parametrical
and convex membership function. In the first part of the
article this essential building block will be defined and its assets and
shortcomings will be discussed.
The novelty regarding the automated, data-driven design strategy
consist in the conservation of the modelling framework when modelling
complex (nonconvex) data-inherent structures. Instead of applying
current clustering methods the design strategy uses the inverse
of the data structure in order to created a fuzzy model solely based
on convex membership functions.
Throughout the article the whole model design process is illustrated,
section by section, with the help of an academic example.
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Serpeka, Rokas. "Analyzing and modelling exchange rate data using VAR framework." Thesis, KTH, Matematik (Inst.), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-94180.
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Abstract In this report analysis of foreign exchange rates time series are performed. First, triangular arbitrage is detected and eliminated from data series using linear algebra tools. Then Vector Autoregressive processes are calibrated and used to replicate dynamics of exchange rates as well as to forecast time series. Finally, optimal portfolio of currencies with minimal Expected Shortfall is formed using one time period ahead forecasts
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Silverwood, Richard Jonathan. "Issues in modelling growth data within a life course framework." Thesis, London School of Hygiene and Tropical Medicine (University of London), 2008. http://researchonline.lshtm.ac.uk/682377/.
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This thesis explores, develops and implements modelling strategies for studying relationships between childhood growth and later health, focusing primarily on the relationship between the development of body mass index (BMI) in childhood and later obesity. Existing growth models are explored, though found to be inflexible and potentially inadequate. Alternative approaches using parametric and nonparametric modelling are investigated. A distinction between balanced and unbalanced data structure is made because of the ways in which missing data can be addressed. A dataset of each type is used for illustration: the Stockholm Weight Development Study (SWEDES) and the Uppsala Family Study (UFS). The focus in each application is obesity, with the first examining how the adiposity rebound (AR), and the second how the adiposity peak (AP) in infancy, relate to later adiposity. In each case a two-stage approach is used. Subject-specific cubic smoothing splines are used in SWEDES to model childhood BMI and estimate the AR for each subject. As childhood BMI data are balanced, missingness can be dealt with via mUltiple imputation. The relationship between the AR and late-adolescent adiposity is then explored via linear and logistic regression, with both the age and BMI at AR found to be strongly and independently associated with late-adolescent adiposity. In the UFS, where childhood BMI data are unbalanced, penalised regression splines are used within a mixed model framework to model childhood BMI and estimate the AP for each subject. The data correlations induced by the family structure of the observations are addressed by fitting multilevel models in the second stage. Both age and BMI at AP are found to be positively associated with later adiposity. The two nonparametric modelling approaches are found to be effective and flexible. Whilst the thesis concentrates on BMI development in childhood and later adiposity, the techniques employed, both in terms the modelling of growth and the relating of the derived features to the outcomes, are far more widely applicable.
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Ekaterina, Guseva. "The Conceptual Integration Modelling Framework: Semantics and Query Answering." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/33464.
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In the context of business intelligence (BI), the accuracy and accessibility of information consolidation play an important role. Integrating data from different sources involves its transformation according to constraints expressed in an appropriate language. The Conceptual Integration Modelling framework (CIM) acts as such a language. The CIM is aimed to allow business users to specify what information is needed in a simplified and comprehensive language. Achieving this requires raising the level of abstraction to the conceptual level, so that users are able to pose queries expressed in a conceptual query language (CQL).
The CIM is comprised of three facets: an Extended Entity Relationship (EER) model
(a high level conceptual model that is used to design databases), a conceptual schema against which users pose their queries, a relational multidimensional model that represents data sources, and mappings between the conceptual schema and sources. Such mappings can be specified in two ways: in the first scenario, the so-called global-as-view (GAV), the global schema is mapped to views over the relational sources by specifying how to obtain tuples of the global relation from tuples in the sources. In the second scenario, sources may contain less detailed information (a more aggregated data) so the local relations are defined as views over global relations that is called as local-as-view (LAV).
In this thesis, we address the problem of expressibility and decidability of queries written in CQL. We first define the semantics of the CIM by translating the conceptual model so we could translate it into a set of first order sentences containing a class of conceptual dependencies (CDs) - tuple-generating dependencies (TGDs) and equality generating dependencies (EGDs), in addition to certain (first order) restrictions to express multidimensionality. Here a multidimensionality means that facts in a data warehouse can be described from different perspectives. The EGDs set the equality between tuples and the TGDs set the rule that two instances are in a subtype association (more precise definitions are given further in the thesis).
We use a non-conflicting class of conceptual dependencies that guarantees a query's decidability. The non-conflicting dependencies avoid an interaction between TGDs and
EGDs. Our semantics extend the existing semantics defined for extended entity relationship models to the notions of fact, dimension category, dimensional hierarchy and dimension attributes.
In addition, a class of conceptual queries will be defined and proven to be decidable.
A DL-Lite logic has been extensively used for query rewriting as it allows us to reduce the complexity of the query answering to AC0. Moreover, we present a query rewriting algorithm for the class of defined conceptual dependencies.
Finally, we consider the problem in light of GAV and LAV approaches and prove the query answering complexities. The query answering problem becomes decidable if we add certain constraints to a well-known set of EGDs + TGDs dependencies to guarantee summarizability. The query answering problem in light of the global-as-a-view approach of mapping has AC0 data complexity and EXPTIME combined complexity. This problem becomes coNP hard if we are to consider it a LAV approach of mapping.
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Mgbemena, Chidozie Simon. "A data-driven framework for investigating customer retention." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13175.
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This study presents a data-driven simulation framework in order to understand customer behaviour and therefore improve customer retention. The overarching system design methodology used for this study is aligned with the design science paradigm. The Social Media Domain Analysis (SoMeDoA) approach is adopted and evaluated to build a model on the determinants of customer satisfaction in the mobile services industry. Furthermore, the most popular machine learning algorithms for analysing customer churn are applied to analyse customer retention based on the derived determinants. Finally, a data-driven approach for agent-based modelling is proposed to investigate the social effect of customer retention. The key contribution of this study is the customer agent decision trees (CADET) approach and a data-driven approach for Agent-Based Modelling (ABM). The CADET approach is applied to a dataset provided by a UK mobile services company. One of the major findings of using the CADET approach to investigate customer retention is that social influence, specifically word of mouth has an impact on customer retention. The second contribution of this study is the method used to uncover customer satisfaction determinants. The SoMeDoA framework was applied to uncover determinants of customer satisfaction in the mobile services industry. Customer service, coverage quality and price are found to be key determinants of customer satisfaction in the mobile services industry. The third contribution of this study is the approach used to build customer churn prediction models. The most popular machine learning techniques are used to build customer churn prediction models based on identified customer satisfaction determinants. Overall, for the identified determinants, decision trees have the highest accuracy scores for building customer churn prediction models.
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Mouline, Ludovic. "Towards a modelling framework with temporal and uncertain data for adaptive systems." Thesis, Rennes 1, 2019. https://ged.univ-rennes1.fr/nuxeo/site/esupversions/32c7a604-bdf6-491e-ba8f-1a9f2a1c0b8b.
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Les systèmes auto-adaptatifs (SAS) optimisent leurs comportements ou configurations au moment de l'exécution en réponse à une modification de leur environnement ou de leurs comportements. Ces systèmes nécessitent donc une connaissance approfondie de la situation en cours qui permet de raisonnement en considérant les opérations d'adaptation. En utilisant la méthodologie de l'Ingénierie Dirigée par les Modèles (IDM), il est possible d'abstraire cette situation. Cependant, les informations concernant le système ne sont pas toujours connues avec une confiance absolue. De plus, dans de tels systèmes, la fréquence de surveillance peut différer du délai nécessaire pour que les mesures de reconfiguration aient des effets mesurables. Ces caractéristiques s'accompagnent d'un défi global pour les ingénieurs logiciels : comment représenter les connaissances incertaines tout en permettant de les interroger efficacement et de représenter les actions en cours afin d'améliorer les processus d'adaptation ? Pour relever ce défi, cette thèse défend la nécessité d'un framework de modélisation qui inclut, en plus de tous les éléments traditionnels, l'incertitude et le temps en tant que concepts de première classe. Par conséquent, un développeur sera en mesure d'extraire des informations relatives au processus d'adaptation, à l'environnement ainsi qu'au système lui-même. Dans cette optique, nous présentons deux contributions évaluées : un modèle de contexte temporel et un langage pour les données incertaines. Le modèle de contexte temporel permet d'abstraire les actions passées, en cours et futures avec leurs impacts et leur contexte. Le langage, appelé Ain'tea, intègre l'incertitude des données en tant que concept de première classeSelf-Adaptive Systems (SAS) optimise their behaviours or configurations at runtime in response to a modification of their environments or their behaviours. These systems therefore need a deep understanding of the ongoing situation which enables reasoning tasks for adaptation operations. Using the model-driven engineering (MDE) methodology, one can abstract this situation. However, information concerning the system is not always known with absolute confidence. Moreover, in such systems, the monitoring frequency may differ from the delay for reconfiguration actions to have measurable effects. These characteristics come with a global challenge for software engineers: how to represent uncertain knowledge that can be efficiently queried and to represent ongoing actions in order to improve adaptation processes? To tackle this challenge, this thesis defends the need for a unified modelling framework which includes, besides all traditional elements, temporal and uncertainty as first-class concepts. Therefore, a developer will be able to abstract information related to the adaptation process, the environment as well as the system itself. Towards this vision, we present two evaluated contributions: a temporal context model and a language for uncertain data. The temporal context model allows abstracting past, ongoing and future actions with their impacts and context. The language, named Ain’tea, integrates data uncertainty as a first-class citizen
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Förster, Stefan. "A formal framework for modelling component extension and layers in distributed embedded systems /." Dresden : TUDpress, 2007. http://www.loc.gov/catdir/toc/fy0803/2007462554.html.
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Duong, Thi V. T. "Efficient duration modelling in the hierarchical hidden semi-Markov models and their applications." Thesis, Curtin University, 2008. http://hdl.handle.net/20.500.11937/1408.
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Modeling patterns in temporal data has arisen as an important problem in engineering and science. This has led to the popularity of several dynamic models, in particular the renowned hidden Markov model (HMM) [Rabiner, 1989]. Despite its widespread success in many cases, the standard HMM often fails to model more complex data whose elements are correlated hierarchically or over a long period. Such problems are, however, frequently encountered in practice. Existing efforts to overcome this weakness often address either one of these two aspects separately, mainly due to computational intractability. Motivated by this modeling challenge in many real world problems, in particular, for video surveillance and segmentation, this thesis aims to develop tractable probabilistic models that can jointly model duration and hierarchical information in a unified framework. We believe that jointly exploiting statistical strength from both properties will lead to more accurate and robust models for the needed task. To tackle the modeling aspect, we base our work on an intersection between dynamic graphical models and statistics of lifetime modeling. Realizing that the key bottleneck found in the existing works lies in the choice of the distribution for a state, we have successfully integrated the discrete Coxian distribution [Cox, 1955], a special class of phase-type distributions, into the HMM to form a novel and powerful stochastic model termed as the Coxian Hidden Semi-Markov Model (CxHSMM). We show that this model can still be expressed as a dynamic Bayesian network, and inference and learning can be derived analytically.Most importantly, it has four superior features over existing semi-Markov modelling: the parameter space is compact, computation is fast (almost the same as the HMM), close-formed estimation can be derived, and the Coxian is flexible enough to approximate a large class of distributions. Next, we exploit hierarchical decomposition in the data by borrowing analogy from the hierarchical hidden Markov model in [Fine et al., 1998, Bui et al., 2004] and introduce a new type of shallow structured graphical model that combines both duration and hierarchical modelling into a unified framework, termed the Coxian Switching Hidden Semi-Markov Models (CxSHSMM). The top layer is a Markov sequence of switching variables, while the bottom layer is a sequence of concatenated CxHSMMs whose parameters are determined by the switching variable at the top. Again, we provide a thorough analysis along with inference and learning machinery. We also show that semi-Markov models with arbitrary depth structure can easily be developed. In all cases we further address two practical issues: missing observations to unstable tracking and the use of partially labelled data to improve training accuracy. Motivated by real-world problems, our application contribution is a framework to recognize complex activities of daily livings (ADLs) and detect anomalies to provide better intelligent caring services for the elderly.Coarser activities with self duration distributions are represented using the CxHSMM. Complex activities are made of a sequence of coarser activities and represented at the top level in the CxSHSMM. Intensive experiments are conducted to evaluate our solutions against existing methods. In many cases, the superiority of the joint modeling and the Coxian parameterization over traditional methods is confirmed. The robustness of our proposed models is further demonstrated in a series of more challenging experiments, in which the tracking is often lost and activities considerably overlap. Our final contribution is an application of the switching Coxian model to segment education-oriented videos into coherent topical units. Our results again demonstrate such segmentation processes can benefit greatly from the joint modeling of duration and hierarchy.
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Duong, Thi V. T. "Efficient duration modelling in the hierarchical hidden semi-Markov models and their applications." Curtin University of Technology, Dept. of Computing, 2008. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=18610.
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Modeling patterns in temporal data has arisen as an important problem in engineering and science. This has led to the popularity of several dynamic models, in particular the renowned hidden Markov model (HMM) [Rabiner, 1989]. Despite its widespread success in many cases, the standard HMM often fails to model more complex data whose elements are correlated hierarchically or over a long period. Such problems are, however, frequently encountered in practice. Existing efforts to overcome this weakness often address either one of these two aspects separately, mainly due to computational intractability. Motivated by this modeling challenge in many real world problems, in particular, for video surveillance and segmentation, this thesis aims to develop tractable probabilistic models that can jointly model duration and hierarchical information in a unified framework. We believe that jointly exploiting statistical strength from both properties will lead to more accurate and robust models for the needed task. To tackle the modeling aspect, we base our work on an intersection between dynamic graphical models and statistics of lifetime modeling. Realizing that the key bottleneck found in the existing works lies in the choice of the distribution for a state, we have successfully integrated the discrete Coxian distribution [Cox, 1955], a special class of phase-type distributions, into the HMM to form a novel and powerful stochastic model termed as the Coxian Hidden Semi-Markov Model (CxHSMM). We show that this model can still be expressed as a dynamic Bayesian network, and inference and learning can be derived analytically.Most importantly, it has four superior features over existing semi-Markov modelling: the parameter space is compact, computation is fast (almost the same as the HMM), close-formed estimation can be derived, and the Coxian is flexible enough to approximate a large class of distributions. Next, we exploit hierarchical decomposition in the data by borrowing analogy from the hierarchical hidden Markov model in [Fine et al., 1998, Bui et al., 2004] and introduce a new type of shallow structured graphical model that combines both duration and hierarchical modelling into a unified framework, termed the Coxian Switching Hidden Semi-Markov Models (CxSHSMM). The top layer is a Markov sequence of switching variables, while the bottom layer is a sequence of concatenated CxHSMMs whose parameters are determined by the switching variable at the top. Again, we provide a thorough analysis along with inference and learning machinery. We also show that semi-Markov models with arbitrary depth structure can easily be developed. In all cases we further address two practical issues: missing observations to unstable tracking and the use of partially labelled data to improve training accuracy. Motivated by real-world problems, our application contribution is a framework to recognize complex activities of daily livings (ADLs) and detect anomalies to provide better intelligent caring services for the elderly.
Coarser activities with self duration distributions are represented using the CxHSMM. Complex activities are made of a sequence of coarser activities and represented at the top level in the CxSHSMM. Intensive experiments are conducted to evaluate our solutions against existing methods. In many cases, the superiority of the joint modeling and the Coxian parameterization over traditional methods is confirmed. The robustness of our proposed models is further demonstrated in a series of more challenging experiments, in which the tracking is often lost and activities considerably overlap. Our final contribution is an application of the switching Coxian model to segment education-oriented videos into coherent topical units. Our results again demonstrate such segmentation processes can benefit greatly from the joint modeling of duration and hierarchy.
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Koutsouris, Alexander. "Building a coherent hydro-climatic modelling framework for the data limited Kilombero Valley of Tanzania." Doctoral thesis, Stockholms universitet, Institutionen för naturgeografi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-142201.
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This thesis explores key aspects for synthesizing data across spatiotemporal scales relevant for water resources management in an Eastern Africa context. Specifically, the potential of large scale global precipitation datasets (GPDs) in data limited regions to overcome spatial and temporal data gaps is considered. The thesis also explores the potential to utilize limited and non-continuous streamflow and stream water chemistry observations to increase hydrological process understanding. The information gained is then used to build a coherent hydro-climatic framework for streamflow modelling. In this thesis, Kilombero Valley Drainage Basin (KVDB) in Tanzania is used as an example of a data limited region targeted for rapid development, intensification and expansion of agriculture. As such, it is representative for many regions across the Eastern Africa. With regards to the data synthesis, two satellite products, three reanalysis products and three interpolated products were evaluated based on their spatial and temporal precipitation patterns. Streamflow data from KVDB and eight subcatchments were then assessed for quality with regards to missing data. Furthermore, recession analysis was used to estimate catchment-scale characteristic drainage timescale. Results from these streamflow analyses, in conjunction with a hydrological tracer-based analysis, were then used for improved understanding of streamflow generation in the region. Finally, a coherent modelling framework using the HBV rainfall-runoff model was implemented and evaluated based on daily streamflow simulation. Despite the challenges of data limited regions and the often large uncertainty in results, this thesis demonstrates that improved process understanding could be obtained from limited streamflow records and a focused hydrochemical sampling when experimental design natural variability were leveraged to gain a large signal to noise ratio. Combining results across all investigations rendered information useful for the conceptualization and implementation of the hydro-climatic modelling framework relevant in Kilombero Valley. For example, when synthesized into a coherent framework the GPDs could be downscaled and used for daily streamflow simulations at the catchment scale with moderate success. This is promising when considering the need for estimating impacts of potential future land use and climate change as well as agricultural intensification.Denna avhandling utforskar aspekter på att syntetisera data med olika rumslig och temporal upplösning, vilket är centralt för vattenförvaltning i östra Afrika. Särskilt fokus ligger på att undersöka möjligheten till att använda globala nederbördsdataset för att fylla rumsliga och temporala luckor där data saknas. Avhandlingen undersökeräven möjligheten till att använda flödesdata med icke-kompletta tidsserier samt kemidata från vattendrag för att utöka kunskap-en om hydrologiska processer. Informationen används för att bygga upp ett integrerande ram-verk för hydro-klimatologisk modellering som exempelvis kan användas för att utforska ef-fekten av ett utökat och intensifierat jordburk på vattenresurser. I denna avhandling användes Kilomberodalens avrinningsområde (Tanzania) som exempel på ett databegränsat område där det pågår en intensiv utökning av jordbruksverksamhet. Detta område kan ses som representa-tivt för ett stort antal områden inom östra Afrika.Datasyntesen innefattade två nederbördsprodukter baserade på satellitdata, tre baserade på återanalysprodukter samt två baserade på interpolering av observervationsdata från regnmä-tare. Dessa åtta produkter utvärderades baserat på deras nederbördsmönster i rum och tid. Ut-över detta utvärderades vattenföringsdata från Kilomberodalens avrinningsområde samt åtta delavrinningsområden utifrån mängden saknad data i respektive tidsserie. Vidare användes resultaten från hydrologisk recessionsanalysför att uppskatta den karaktäristiska avrinningsti-den för avrinningsområden. Resultaten från recessionsanalysensamthydrologiskt spårämnes-försök användessedan för att utöka kunskapen om avrinningsbildning och vattenföring i om-rådet samt som stöd i valet av hydrologiskt modelleringsverktyg. Avslutningsvis användes HBV-avrinningsmodellen för att simulera daglig vattenföring. Trots utmaningen i att arbeta iett databegränsat område och de osäkerheter i resultat som detta tenderar att leda till visar resultaten att det var möjligt att använda begränsad vattenfö-ringsdata och vattenkemidata för att utöka den hydrologiska processförståelsen av området. Detta möjliggjordes genom ett experimentellt upplägg som utnyttjade till ett stort signal-till-brusförhållande under rådande förhållanden av naturlig variabilitet. Kombinerade resultat från alla genomförda studier kunde utnyttjas vid konceptualiseringen och implementeringen av ramverket för hydroklimatologisk modellering av Kilomberodalens avrinningsområde. Till exempel kunde de globala nederbördsdataseten användas för lokal modellering av flödesdata med viss framgång efter syntes och implementering i det integrerande ramverket för hydro-klimatologisk modellering. Detta är lovande med tanke på behovet av att undersöka vilken påverkan möjliga framtida förändringar i markanvändning, klimat samt jordbruk har på den lokala och regionala miljön.
At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Manuscript. Paper 4: Manuscript.
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Dale, Anthony James Eric. "A Framework for Linking Projects and Project Management Methods." Thesis, University of Canterbury. Computer Science and Software Engineering, 2006. http://hdl.handle.net/10092/1182.
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Software development processes such as the Waterfall process and Extreme Programming are Project Management Methods (PMMs) which are well known and widely used. However, conventional Project Management (PM) lacks the process concepts expressed in PMMs, and the connection between PMMs and PM is not much explored in the literature. Our research problem is to make this connection. We present data models for PM and PMM, in a framework that can articulate the PM-to-PMM relationship, illustrating with simple examples. Tools and visualizations created in terms of our framework can make use of the familiarity, history and context of project management tools, and the prescriptiveness and reactivity of PMMs, and we believe these may assist the management of complicated projects, such as IT projects. Project Mentor, a prototype Java/XML implementation of the framework semantics, can create and then revise a PMM-aware project, conforming to a specified PMM. The PM-to-PMM connection is persistent in project data, and we describe a visualization of the footsteps of a PMM in project data that does not rely on the state of a PMM process. The visualization can also be used by Project Mentor, to indicate the state of a PMM. We test for possible applications of our framework with a case study and survey of some existing project data, and conclude with a description of further work.
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Lavassani, Mehrzad. "Reliable Information Exchange in IIoT : Investigation into the Role of Data and Data-Driven Modelling." Licentiate thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-34886.
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The concept of Industrial Internet of Things (IIoT) is the tangible building block for the realisation of the fourth industrial revolution. It should improve productivity, efficiency and reliability of industrial automation systems, leading to revenue growth in industrial scenarios. IIoT needs to encompass various disciplines and technologies to constitute an operable and harmonious system. One essential requirement for a system to exhibit such behaviour is reliable exchange of information. In industrial automation, the information life-cycle starts at the field level, with data collected by sensors, and ends at the enterprise level, where that data is processed into knowledge for business decision making. In IIoT, the process of knowledge discovery is expected to start in the lower layers of the automation hierarchy, and to cover the data exchange between the connected smart objects to perform collaborative tasks. This thesis aims to assist the comprehension of the processes for information exchange in IIoT-enabled industrial automation- in particular, how reliable exchange of information can be performed by communication systems at field level given an underlying wireless sensor technology, and how data analytics can complement the processes of various levels of the automation hierarchy. Furthermore, this work explores how an IIoT monitoring system can be designed and developed. The communication reliability is addressed by proposing a redundancy-based medium access control protocol for mission-critical applications, and analysing its performance regarding real-time and deterministic delivery. The importance of the data and the benefits of data analytics for various levels of the automation hierarchy are examined by suggesting data-driven methods for visualisation, centralised system modelling and distributed data streams modelling. The design and development of an IIoT monitoring system are addressed by proposing a novel three-layer framework that incorporates wireless sensor, fog, and cloud technologies. Moreover, an IIoT testbed system is developed to realise the proposed framework. The outcome of this study suggests that redundancy-based mechanisms improve communication reliability. However, they can also introduce drawbacks, such as poor link utilisation and limited scalability, in the context of IIoT. Data-driven methods result in enhanced readability of visualisation, and reduced necessity of the ground truth in system modelling. The results illustrate that distributed modelling can lower the negative effect of the redundancy-based mechanisms on link utilisation, by reducing the up-link traffic. Mathematical analysis reveals that introducing fog layer in the IIoT framework removes the single point of failure and enhances scalability, while meeting the latency requirements of the monitoring application. Finally, the experiment results show that the IIoT testbed works adequately and can serve for the future development and deployment of IIoT applications.SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)
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Doherty, Sean T. "The household activity-travel scheduling process, computerized survey data collection and the development of a unified modelling framework." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0018/NQ53889.pdf.
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Pajak, Maciej. "Evolutionary conservation and diversification of complex synaptic function in human proteome." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/31108.
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The evolution of synapses from early proto-synaptic protein complexes in unicellular eukaryotes to sophisticated machines comprising thousands of proteins parallels the emergence of finely tuned synaptic plasticity, a molecular correlate for memory and learning. Phenotypic change in organisms is ultimately the result of evolution of their genotype at the molecular level. Selection pressure is a measure of how changes in genome sequence that arise though naturally occurring processes in populations are fixed or eliminated in subsequent generations. Inferring phylogenetic information about proteins such as the variation of selection pressure across coding sequences can provide valuable information not only about the origin of proteins, but also the contribution of specific sites within proteins to their current roles within an organism. Recent evolutionary studies of synaptic proteins have generated attractive hypotheses about the emergence of finely-tuned regulatory mechanisms in the post-synaptic proteome related to learning, however, these analyses are relatively superficial. In this thesis, I establish a scalable molecular phylogenetic modelling framework based on three new inference methodologies to investigate temporal and spatial aspects of selection pressure changes for the whole human proteome using protein orthologs from up to 68 taxa. Temporal modelling of evolutionary selection pressure reveals informative features and patterns for the entire human proteome and identifies groups of proteins that share distinct diversification timelines. Multi-ontology enrichment analysis of these gene cohorts was used to aid biological interpretation, but these approaches are statistically under powered and do not capture a clear picture of the emergence of synaptic plasticity. Subsequent pathway-centric analysis of key synaptic pathways extends the interpretation of temporal data and allows for revision of previous hypotheses about the evolution of complex synaptic function. I proceed to integrate inferred selection pressure timeline information in the context of static protein-protein interaction data. A network analysis of the full human proteome reveals systematic patterns linking the temporal profile of proteins’ evolution and their topological role in the interaction graph. These graphs were used to test a mechanistic hypothesis that proposed a propagating diversification signal between interactors using the temporal modelling data and network analysis tools. Finally, I analyse the data of amino-acid level spatial modelling of selection pressure events in Arc, one of the master regulators of synaptic plasticity, and its interactors for which detailed experimental data is available. I use the Arc interactome as an example to discuss episodic and localised diversifying selection pressure events in tightly coupled complexes of protein and showcase potential for a similar systematic analysis of larger complexes of proteins using a pathway-centric approach. Through my work I revised our understanding of temporal evolutionary patterns that shaped contemporary synaptic function through profiling of emergence and refinement of proteins in multiple pathways of the nervous system. I also uncovered systematic effects linking dependencies between proteins with their active diversification, and hypothesised about their extension to domain level selection pressure events.
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Rhodes, S. J. "The development of a mathematical modelling framework to translate TB vaccine responses between species and predict the most immunogenic dose in humans using animal data." Thesis, London School of Hygiene and Tropical Medicine (University of London), 2018. http://researchonline.lshtm.ac.uk/4647153/.
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Background: Preclinical animal experiments measuring vaccine immunogenicity and safety are essential, not only to establish if the vaccine should progress further, but to generate information on how the vaccine should be administered in humans. Animal models that represent human vaccine responses well are vital to translate information about vaccine dose to clinical phases. Vaccine dose is a key aspect in creating an effective vaccine. However, if the wrong dose is chosen, vaccine candidates may be mistakenly discarded and considerable resources wasted. Current methods of finding optimal vaccine dose are mostly empirically based, which may be leading to sub-optimal doses progressing into later clinical trials. A current example of this is in the tuberculosis (TB) vaccine developmental pipeline, where a series of adjuvanted subunit vaccines, the H-series, have progressed through to later stages of clinical development with a high dose that has been shown to less immunogenic than lower doses. In drug development, mathematical model-based methods are routinely used alongside empirical evaluations, to inform dose-finding. I hypothesised that vaccine development may benefit from the application of similar quantitative methods. As such, I launched the new field of vaccine immunostimulation/immunodynamic (IS/ID) mathematical modelling. My aims for this thesis were 1) to establish differences in Bacillus Calmette–Guérin (BCG) Interferon-Gamma (IFN-γ) response by human subpopulation, then develop a IS/ID model to represent these response dynamics and identify the most representative macaque subpopulation for human BCG responses. Aim 2) was to predict human H-series vaccine IFN-γ response using IS/ID model calibrated to mouse multi-dose IFN-γ data and allometric scaling. Methods: For aim 1, longitudinal data on IFN-γ emitting CD4+ T cells following vaccination BCG were available in humans and macaques. Human (sub)population covariates were: baseline BCG vaccination status, time since BCG vaccination, gender and monocyte/lymphocyte cell count ratio. The macaque (sub)population covariate was colony of origin. I developed a two-compartmental mathematical model describing the post-BCG IFN-γ immune response dynamics. The model was calibrated to the human and macaque data using 4 Nonlinear Mixed Effects Modelling (NLMEM) to establish if there were differences in IFN-γ dynamics for both species subpopulations. I then established which macaque subpopulation best described human data. For aim 2, longitudinal data on IFN-γ emitting CD4+ T cells following two vaccinations with five doses of novel TB vaccine H56+IC31 in mice were generated. I then assessed the shape of the dose response curve at early and late time points. I calibrated the T cell model to the mouse data and established the change in key model parameters across dose. Using the change in model parameters across dose found in the mice, I predicted the immune response dynamics in humans for different doses and which dose was most immunogenic. Results: In aim 1, I found that BCG status in humans (baseline BCG-naïve or baseline BCG-vaccinated) was associated with differences in the peak and end IFN-γ response after vaccination with BCG. When the mathematical model was calibrated to the BCG data for both macaques and humans, significant differences (p < 0.05) in key model parameters were found after stratification by macaque colony and human baseline-BCG status. Indonesian cynomolgus macaques had the closest immune response dynamics to the baseline BCG-naïve humans. In aim 2, a peaked curve was the best description of the mouse H56+IC31 dose response curve for early and late time points. Calibrating a revaccination model to the data and mapping changes in the estimated mouse model parameters across dose group to the estimated human model parameters, I found at day 224 (a latest time point), the model-predicted median number of human IFN-γ secreting CD4+ T cells were the highest for the dose group in the range 1-10μg H56/H1+500 nmol IC31. This suggests a dose of 1-10μg may be the most immunogenic in humans. Discussion: Finding the most predictive animal model and optimal vaccine dose is essential for efficiently accelerating the development of new, effective, TB vaccines. I demonstrated that mathematical modelling was a useful tool to quantify BCG immune response dynamics in macaques and humans. I established which macaque subpopulation should be used to represent a human BCG (or potentially new TB vaccine) induced IFN-γ response in future clinical trials. Using IFN-γ as marker of vaccine immunogenicity, mathematical modelling predictions using preclinical data suggested that doses in current novel TB vaccines clinical 5 trials on healthy BCG-vaccinated participants should be between 1-10μg H56/H1+500 nmol IC31, a result which has been recently corroborated in an empirical H56+IC31 dose-ranging trial. This project has demonstrated the potential utility of mathematical modelling in vaccine development. I believe future work on IS/ID modelling should include data on more complex immune response networks and different animal and human subpopulations. This future work is entirely feasible and would establish IS/ID modelling as a legitimate tool to accelerate vaccine development.
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Mallo, Angelina. "Problem – Orsak – Konsekvens (POK)-Modellen för mjukvaruutvecklingsprojekt." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-227850.
Full textAbstract:
Det blir allt vanligare att arbeta i projekt och därmed finns alltfler mjukvaruutvecklingsmetoder eller ramverk att applicera till projektet. Det är dock fortfarande inte ovanligt att man arbetar metodlöst, vilket kan leda till att oberäknade problem uppstår. En arbetsmetod eller ett metodramverk formar projektet så att man på bästa skall kunna undvika problem samt vara medveten om problem som skulle kunna uppstå. Syftet med den här studien är att ta fram en modell som identifierar problem och dess orsaker och konsekvenser som uppstår i ett mjukvaruutvecklingsprojekt med hjälp av ramverk. Ramverken som används i den här studien är Essence – Kernel and Languages for Software Engineering Methods och Self-Governance Developer Framework. Målet är att den här modellen skall användas av personer inom mjukvaruutveckling för projekt eller forskning. Studien är av kvalitativ natur med induktiv ansats. Det utfördes ett mjukvaruprojekt där teamet arbetade metodlöst och identifierade problem från en uppföljning som gjordes aktivt under projektets arbetsgång. Resultatet av studien är en modell som innebär att man skall kunna hitta orsaker samt konsekvenser till uppstådda problem inom projektet. Modellen som har tagits fram heter Problem-Orsak-Konsekvens-modellen och förkortas POK-modellen.It is becoming more common to work in projects and therefor there are more and more software development methods to apply for the project. However, it is still not unusual to be working ad hoc, which can lead to uncalculated problems. A method or a framework shapes the project so that problems can be avoided in best possible way. It also helps developers to be aware of the problem that could arise. Despite this, there is no compilation of “anticipated problems” when working ad hoc. The purpose of this study is to produce a model to identify problems, root cause of problems and consequences of the problems that can occur when working in a software development project with the help from frameworks. The frameworks used in this study are Essence – Kernel and Languages for Software Engineering Methods and Self- Governance Developer Framework. The goal is that the model is used in software development environments for projects or research. The study is of qualitative nature with inductive approach. A software project was performed where the team worked without a method and identified problems from a follow-up that was active during the workflow of the project. The result of the study is a model, which should be able to find the source to occurred problems as well as consequence within the project.
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Blomberg, Per. "Informell Statistisk Inferens i modelleringssituationer : En studie om utveckling av ett ramverk för att analysera hur elever uttrycker inferenser." Licentiate thesis, Linnéuniversitetet, Institutionen för matematikdidaktik (MD), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-45572.
Full textAbstract:
Syftet med denna studie är att bidra med ökad kunskap om lärande och undervisning i informell statistisk inferens. I studien användes en kvalitativ forskningsstrategi inriktad mot prövning och generering av teorier med inspiration av grounded theory. Studiens kunskapsfokus är riktad mot karakterisering av statistiska processer och begrepp där system av begreppsramverk om informell statistisk inferens och modellering utgör en central del av forskningen. För att erhålla adekvat empiri utformades en undervisningssituation där elever engagerades med att planera och genomföra en undersökning. Studien genomfördes i en normal klassrumssituation där undervisningen inriktades mot ett område inom sannolikhet och statistisk där bland annat lådagram och normalfördelning med tillhörande begrepp introduceras. Det empiriska materialet samlades in genom videoinspelning och skriftliga redovisningar. Materialet analyserades genom ett sammansatt ramverk om informell statistisk inferens och modellering. Resultatet av analysen visar exempel på hur elever kan förväntas uttrycka aspekter av informella statistisk inferens då de genomför statistiska undersökningar. Vidare utvecklades ett ramverk som teoretiskt beskriver informell statistisk inferens i modelleringssituationer. Studien pekar på att ISI-modellering har potential att användas för att analysera hur informell statistisk inferens kan komma till uttryck och att identifiera potentiella inlärningsmöjligheter för studenter att utveckla sin förmåga att uttrycka informella statistisk slutledning och att identifiera potentiella inlärningsmöjligheter för elever att utveckla sin förmåga att uttrycka informella inferenser.The purpose of this study is to improve our knowledge about teaching and learning of informal statistical inference. A qualitative research strategy is used in the study that focuses on the testing and generation of theories inspired by grounded theory. The knowledge focus of the study is aimed at the characterisation of statistical processes and concepts where systems of concept frameworks about informal statistical inference and modelling represent an essential part of the research. In order to obtain adequate empirical data, a teaching situation was devised whereby students were involved in planning and implementing an investigation. The study was conducted in a normal classroom situation where the teaching was focused on an area in probability and statistics that included the introduction of box plots and normal distribution with related concepts. The empirical material was collected through video recordings and written reports. The material was analysed using a combined framework of informal statistical inference and modelling. The results of the analysis highlight examples of how students can be expected to express aspects of informal statistical inference within the context of statistical inquiry. A framework was also developed aimed to theoretically depict informal statistical inference in modelling situations. The study suggests that this framework has the potential to be used to analyse how informal statistical inference of students are expressed and to identify potential learning opportunities for students to develop their ability to express inferences.
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Grippa, Taïs. "Very‑high resolution earth observation data and open‑source solutions for mapping urban areas in sub-Saharan Africa. Implementation of an operational framework for production of geoinformation. Application on Ouagadougou (Burkina Faso) and Dakar (Senegal)." Doctoral thesis, Universite Libre de Bruxelles, 2019. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/284464.
Full textAbstract:
Nowadays, in sub-Saharan Africa (SSA), about 40% of the population is urban and this region is expected to face the highest growth rates during the next decades. By 2100, the three most populated cities in the world will be located in SSA. As a consequence of the extremely fast transformations experienced during the last decades, SSA cities are facing social and environmental issues combined with a lack of financial means and capacity in urban planning and management. The poorest often constitute a large part of the urban population that is extremely vulnerable to health and disaster risks.In SSA cities, up-to-date and spatially detailed geographic information is often missing. This lack of information is an important issue for many scientific studies focusing on different urban issues and there is a real need to improve the availability of geoinformation for these cities in order to support urban planning, urban management, environment monitoring, epidemiology or risk assessment, etc. The work presented in this thesis aims to develop different frameworks for the production of geoinformation. For this purpose, advantage is taken of Very-High Resolution Remote Sensing imagery (0.5 meters) and open-source software. These frameworks implement cutting-edge methods and can handle a large amount of data in a semi-automated fashion to produce maps covering very large areas of interest. In the spirit of open science, the processing chains are entirely based on open-source software and are released publicly in open-access for any interested researchers, in order to make the methods developed completely transparent and in order to contribute to the creation of a pool of common tools and scientific knowledge. These frameworks are used to produce very detailed land-cover and land-use maps that provide essential information such as the built-up density, or the fact that a neighborhood is residential or not. This detailed geoinformation is then used as indicators of presence of populated places to improve existing population models at the intra-urban level.Option Géographie du Doctorat en Sciences
info:eu-repo/semantics/nonPublished
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Albiol, Graullera Pablo. "Architecture Design and Interoperability Analysisof a SCADA System for the Power Network Control and Management." Thesis, KTH, Mekatronik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217798.
Full textAbstract:
SCADA-system (Supervisory Control and Data Acquisition) har under de senaste decennierna använts i stor utsträckning, med utmärkta resultat för nätverksdrift och -förvaltning. Kunder ställer emellertid krav på att SCADA-system ska kunna integrera externa komponenter för att möjliggöra utveckling av befintliga och nya affärsprocesser. Det innebär att dessa system utvecklas från en monolitisk infrastruktur till en löst kopplad och flexibel arkitektur. Således har nya behov uppstått för att förbättra systemets interoperabilitet, minska komplexiteten och förbättra underhållet. Föreliggande masterprojekt presenterar ett ramverk för att förutsäga systems interoperabilitetet (IPF); ett ramverk som stöder arkitekturprocessen under de tidiga stadierna av produktutveckling. Vidare har arbetet undersökt några alternativa arkitekturer, vilka har modellerats och verifierats med hjälp av ovannämnda ramverk. En första konceptuell arkitektur har utvecklats för att förbättra interoperabiliteten hos interna system, för att reducera kopplingen mellan det grundläggande SCADA-systemet och Energy Management-systemet (EMS). Därefter genererades en andra arkitektur som möjliggör integration av externa komponenter för att främja den externa interoperabiliteten. Resultat visar att de föreslagna arkitekturerna är korrekta (enligt IPF) och systemets driftskompatibilitet förbättras. Vidare förefaller den slutligt föreslagna lösningen vara mindre komplex än den nuvarande arkitekturen på lång sikt, men det skulle behövas en större insats och väsentliga förändringar för att uppgradera systemarkitekturen.SCADA (Supervisory Control and Data Acquisition) systems have been widely used during the last decades delivering excellent results for the power network operation and management. However, some current customer requirements are for SCADA systems to integrate external components in order to perform advanced power network studies and develop both existing and new business processes. This novel viewpoint will make these systems evolve from a monolithic infrastructure towards a loosely coupled and flexible architecture. Hence, new needs have arisen with the aim of improving the system interoperability, reducing the complexity and enhancing the maintainability. This master´s thesis project presents an Interoperability Prediction Framework (IPF), that supports the architecture design process during the early stages of product development. In addition, this work has also investigated some alternative architectures, which have been modelled and verified using the previously mentioned framework. A first conceptual architecture has been designed to improve the internal system interoperability, reducing the coupling between the basic SCADA and the Energy Management System (EMS). Later, a second architecture that allows the integration of external components has been introduced to promote the external interoperability. Results show that the proposed architectures are correct (according to the IPF) and the interoperability of the system is improved. Furthermore, initial conclusions suggest that the final proposed solution would be less complex than the current architecture in the long term, although a large effort and substantial changes would be needed to upgrade the system architecture.
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Teng, Sin Yong. "Intelligent Energy-Savings and Process Improvement Strategies in Energy-Intensive Industries." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-433427.
Full textAbstract:
S tím, jak se neustále vyvíjejí nové technologie pro energeticky náročná průmyslová odvětví, stávající zařízení postupně zaostávají v efektivitě a produktivitě. Tvrdá konkurence na trhu a legislativa v oblasti životního prostředí nutí tato tradiční zařízení k ukončení provozu a k odstavení. Zlepšování procesu a projekty modernizace jsou zásadní v udržování provozních výkonů těchto zařízení. Současné přístupy pro zlepšování procesů jsou hlavně: integrace procesů, optimalizace procesů a intenzifikace procesů. Obecně se v těchto oblastech využívá matematické optimalizace, zkušeností řešitele a provozní heuristiky. Tyto přístupy slouží jako základ pro zlepšování procesů. Avšak, jejich výkon lze dále zlepšit pomocí moderní výpočtové inteligence. Účelem této práce je tudíž aplikace pokročilých technik umělé inteligence a strojového učení za účelem zlepšování procesů v energeticky náročných průmyslových procesech. V této práci je využit přístup, který řeší tento problém simulací průmyslových systémů a přispívá následujícím: (i)Aplikace techniky strojového učení, která zahrnuje jednorázové učení a neuro-evoluci pro modelování a optimalizaci jednotlivých jednotek na základě dat. (ii) Aplikace redukce dimenze (např. Analýza hlavních komponent, autoendkodér) pro vícekriteriální optimalizaci procesu s více jednotkami. (iii) Návrh nového nástroje pro analýzu problematických částí systému za účelem jejich odstranění (bottleneck tree analysis – BOTA). Bylo také navrženo rozšíření nástroje, které umožňuje řešit vícerozměrné problémy pomocí přístupu založeného na datech. (iv) Prokázání účinnosti simulací Monte-Carlo, neuronové sítě a rozhodovacích stromů pro rozhodování při integraci nové technologie procesu do stávajících procesů. (v) Porovnání techniky HTM (Hierarchical Temporal Memory) a duální optimalizace s několika prediktivními nástroji pro podporu managementu provozu v reálném čase. (vi) Implementace umělé neuronové sítě v rámci rozhraní pro konvenční procesní graf (P-graf). (vii) Zdůraznění budoucnosti umělé inteligence a procesního inženýrství v biosystémech prostřednictvím komerčně založeného paradigmatu multi-omics.
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Mochão, Hugo Daniel Cepeda. "Improvement of KiMoSys framework for kinetic modelling." Master's thesis, 2021. http://hdl.handle.net/10362/118280.
Full textAbstract:
Over the past years, an increasing amount of biological data produced shows the impor tance of data repositories. The databases ensure an easier way to reuse and share research
data between the scientific community. Among the most important features are the quick
access to data, described by metadata and available in standard formats, and the compli ance with the FAIR (Findable, Accessible, Interoperable and Reusable) Data Principles
for data management.
KiMoSys (https://kimosys.org) is a public domain-specific repository of experi mental data, containing concentration data of enzymes, metabolites and flux data. It
offers a web-based interface and upload facility to publish data, making it accessible in
standard formats, while also integrating kinetic models related to the data.
This thesis is a contribution to the improvement and extension of KiMoSys. It includes
the addition of more downloadable data formats, the introduction of data visualization,
the incorporation of more tools to filter data, the integration of a simulation environment
for kinetic models and the inclusion of a unique persistent identifier system.
As a result, it is provided a new version of KiMoSys, with a renewed interface, mul tiple new features, and an enhancement of the previously existing ones. These are in
accordance with all FAIR data principles. Therefore, it is believed that KiMoSys v2.0 will
be an important tool for the systems biology modeling community.Nos últimos anos, uma quantidade crescente de dados biológicos produzidos atesta a importância dos repositórios de dados. As bases de dados garantem uma maneira mais fácil de reutilizar e partilhar dados de pesquisa entre a comunidade científica. Entre as características mais importantes estão o rápido acesso aos dados, descritos por metada dos e disponíveis em formatos padrão, e o cumprimento dos Princípios FAIR (Findable, Accessible, Interoperable e Reusable) para a gestão de dados. KiMoSys (https://kimosys.org) consiste num repositório público de domínio espe cífico de dados experimentais, contendo dados de concentração de enzimas, metabolitos e dados de fluxo. Oferece uma interface para a web e uma ferramenta de carregamento de dados, tornando-os acessíveis em formatos padrão, além de integrar modelos cinéticos relacionados aos dados. Esta tese contribui para o melhoramento e extensão do KiMoSys. Inclui a adição de mais formatos de dados para descarga, a introdução de visualização de dados, a incorpo ração de mais opções para filtrar os dados, a integração de um ambiente de simulação para modelos cinéticos e a inclusão de um sistema de identificador único persistente. Como resultado, é apresentada uma nova versão do KiMoSys, com uma interface renovada, várias novas características e um aprimoramento das anteriormente existentes. Estas estão de acordo com todos os princípios de dados FAIR. Portanto, acredita-se que o KiMoSys v2.0 será uma ferramenta importante para a comunidade de modelagem de sistemas biológicos.
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Zhu, Lingkai. "SemDQ: A Semantic Framework for Data Quality Assessment." Thesis, 2014. http://hdl.handle.net/10012/8558.
Full textAbstract:
Objective:
Access to, and reliance upon, high quality data is an enabling cornerstone of modern health delivery systems. Sadly, health systems are often awash with poor quality data which contributes both to adverse outcomes and can compromise the search for new knowledge. Traditional approaches to purging poor data from health information systems often require manual, laborious and time-consuming procedures at the collection, sanitizing and processing stages of the information life cycle with results that often remain sub-optimal. A promising solution may lie with semantic technologies - a family of computational standards and algorithms capable of expressing and deriving the meaning of data elements. Semantic approaches purport to offer the ability to represent clinical knowledge in ways that can support complex searching and reasoning tasks. It is argued that this ability offers exciting promise as a novel approach to assessing and improving data quality. This study examines the effectiveness of semantic web technologies as a mechanism by which high quality data can be collected and assessed in health settings. To make this assessment, key study objectives include determining the ability to construct of valid semantic data model that sufficiently expresses the complexity present in the data as well as the development of a comprehensive set of validation rules that can be applied semantically to test the effectiveness of the proposed semantic framework.
Methods:
The Semantic Framework for Data Quality Assessment (SemDQ) was designed. A core component of the framework is an ontology representing data elements and their relationships in a given domain. In this study, the ontology was developed using openEHR standards with extensions to capture data elements used in for patient care and research purposes in a large organ transplant program. Data quality dimensions were defined and corresponding criteria for assessing data quality were developed for each dimension. These criteria were then applied using semantic technology to an anonymized research dataset containing medical data on transplant patients. Results were validated by clinical researchers. Another test was performed on a simulated dataset with the same attributes as the research dataset to confirm the computational accuracy and effectiveness of the framework.
Results:
A prototype of SemDQ was successfully implemented, consisting of an ontological model integrating the openEHR reference model, a vocabulary of transplant variables and a set of data quality dimensions. Thirteen criteria in three data quality dimensions were transformed into computational constructs using semantic web standards. Reasoning and logic inconsistency checking were first performed on the simulated dataset, which contains carefully constructed test cases to ensure the correctness and completeness of logical computation. The same quality checking algorithms were applied to an established research database. Data quality defects were successfully identified in the dataset which was manually cleansed and validated periodically. Among the 103,505 data entries, application of two criteria did not return any error, while eleven of the criteria detected erroneous or missing data, with the error rates ranging from 0.05% to 79.9%. Multiple review sessions were held with clinical researchers to verify the results. The SemDQ framework was refined to reflect the intricate clinical knowledge. Data corrections were implemented in the source dataset as well as in the clinical system used in the transplant program resulting in improved quality of data for both clinical and research purposes.
Implications:
This study demonstrates the feasibility and benefits of using semantic technologies in data quality assessment processes. SemDQ is based on semantic web standards which allows easy reuse of rules and leverages generic reasoning engines for computation purposes. This mechanism avoids the shortcomings that come with proprietary rule engines which often make ruleset and knowledge developed for one dataset difficult to reuse in different datasets, even in a similar clinical domain. SemDQ can implement rules that have shown to have a greater capacity of detect complex cross-reference logic inconsistencies. In addition, the framework allows easy extension of knowledge base to cooperate more data types and validation criteria. It has the potential to be incorporated into current workflow in clinical care setting to reduce data errors during the process of data capture.
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Sabelnykova, Veronica. "Bayesian methods for joint modelling of survival and longitudinal data: applications and computing." Thesis, 2012. http://hdl.handle.net/1828/4378.
Full textAbstract:
Multi-state models are useful for modelling progression of a disease, where states represent the health status of a subject under study. In practice, patients may be observed when necessity strikes thus implying that the disease and observation processes are not independent. Often, clinical visits are postponed or advanced by the clinician or patient themselves based on the health status of the patient. In such situations, there is a potential for the frequency and timing of the examinations to be dependent on the latent transition times, and the observation process may be informative. We consider the case where the exact times of transitions between health states of the patient are not observed and so the disease process is interval censored. We model the disease and observation processes jointly to ensure valid inference. The transition intensities are modelled assuming proportional hazards and we link the two processes via random effects. Using a Bayesian framework we apply our joint model to the analysis of a large study examining cancer trajectories of palliative care patients.Graduate
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"IISS: A Framework to Influence Individuals through Social Signals on a Social Network." Master's thesis, 2014. http://hdl.handle.net/2286/R.I.24884.
Full textAbstract:
abstract: Contemporary online social platforms present individuals with social signals in the form of news feed on their peers' activities. On networks such as Facebook, Quora, network operator decides how that information is shown to an individual. Then the user, with her own interests and resource constraints selectively acts on a subset of items presented to her. The network operator again, shows that activity to a selection of peers, and thus creating a behavioral loop. That mechanism of interaction and information flow raises some very interesting questions such as: can network operator design social signals to promote a particular activity like sustainability, public health care awareness, or to promote a specific product? The focus of my thesis is to answer that question. In this thesis, I develop a framework to personalize social signals for users to guide their activities on an online platform. As the result, we gradually nudge the activity distribution on the platform from the initial distribution p to the target distribution q. My work is particularly applicable to guiding collaborations, guiding collective actions, and online advertising. In particular, I first propose a probabilistic model on how users behave and how information flows on the platform. The main part of this thesis after that discusses the Influence Individuals through Social Signals (IISS) framework. IISS consists of four main components: (1) Learner: it learns users' interests and characteristics from their historical activities using Bayesian model, (2) Calculator: it uses gradient descent method to compute the intermediate activity distributions, (3) Selector: it selects users who can be influenced to adopt or drop specific activities, (4) Designer: it personalizes social signals for each user. I evaluate the performance of IISS framework by simulation on several network topologies such as preferential attachment, small world, and random. I show that the framework gradually nudges users' activities to approach the target distribution. I use both simulation and mathematical method to analyse convergence properties such as how fast and how close we can approach the target distribution. When the number of activities is 3, I show that for about 45% of target distributions, we can achieve KL-divergence as low as 0.05. But for some other distributions KL-divergence can be as large as 0.5.Dissertation/Thesis
M.S. Computer Science 2014
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Musenge, Eustasius. "Modelling spatiotemporal patterns of childhood HIV/TB related mortality and malnutrition: applications to Agincourt data in rural South Africa." Thesis, 2014.
Find full textAbstract:
Background: South Africa accounts for more than a seventh of the global population living
with HIV/AIDS and TB, and ranks highest in HIV/TB co-infection worldwide. Consequent
high child mortality is exacerbated by child malnutrition, which is an important indicator of
health status and is associated with morbidity as well as mortality. Rural areas usually present with the greatest burden of morbidity and mortality, yet the extent of geographical disparities in child mortality, malnutrition and HIV/TB has hardly been explored. This is a reservoir of information useful for effective public health interventions. In this thesis we investigated the factors associated with childhood HIV/TB mortality and malnutrition, how they interrelate and their spatial distribution in the rural Agincourt sub-district located in north-east South Africa close to the border with Mozambique.
Rationale: Africa at large lacks data that are routinely and reliably collected then validated, to guide policy and intervention programmes. Causes of deaths and even death counts are often misclassified and underestimated respectively, especially for children. To bridge this gap, a health and socio-demographic surveillance systems located in the rural Agincourt sub-district hosts which annually collects and collates data on vital events including fertility, mortality and migration. These data have been collected since 1992 to-date and now cover 80,000 people living in more than 16,000 households situated in 27 villages; all households are fully
geo-coded. These hierarchical data allow us to address several epidemiological questions on how person, place (spatial) and time (temporality) have impacted on mortality and
malnutrition patterns in children living in the rural Agincourt sub-district.
Objectives: The aims of this thesis were both methodological and applied:
Methodological
(1) To investigate the presence of spatial autocorrelation in the Agincourt sub-district
and model this using geographical and geo-statistical procedures
(2) To model large spatial random effects accurately and efficiently
(3) To model hierarchical data with zero inflated outcomes
Applied
(1) To investigate childhood HIV/TB mortality determinants and their geographical
distribution using retrospective and cross-sectional data
(2) To determine factors associated with malnutrition outcomes adjusting for their
multivariate spatial random effects and selection bias for children under five years
(3) To model how the associated factors were interrelated as either underlying or
proximate factors of child mortality or malnutrition using pathway analysis.
Methods: We conducted a secondary data analysis based on retrospective and cross-sectional data collected from 1992 to 2010 from the Agincourt sub-district in rural northeast South Africa. During the period of our study 71,057 children aged 0 to 9 years from 15,703 households were observed. All the data in the thesis were for children aged 1 to under 5 except for the chapter 6 (last paper) who were aged from 0 to 9 years of age. Child HIV/TB death and malnutrition were the outcome measures; mortality was derived from physicianbased verbal autopsy. We investigated presence of spatial autocorrelation using Moran’s and Geary’s coefficients, semi-variograms and estimated the spatial parameters using Bayesianbased univariate and multivariate procedures. Regression modelling that adjusted for spatial random effects was done using linear regression and zero inflated variants for logistic, Poisson and Negative Binomial regression models. Structural equation models were used in modelling the complex relationships between multiple exposures and child HIV/TB mortality and malnutrition portrayed by conceptual frameworks. Risk maps were drawn based on spatial residuals (posteriors) with prediction (kriging) procedures used to estimate for households where no data were observed. Statistical inference on parameter estimation was done using both the frequentist; maximum likelihood estimation and Bayesian; Markov Chain Monte Carlo (MCMC) directly and sometimes aided with Metropolis Hastings or Integrated
Nested Laplace Approximations (INLA).
Results: The levels of child under-nutrition in this area were: 6.6% wasted, 17.3% stunted
and 9.9% underweight. Moran’s (I) and Geary’s (c) coefficients indicated that there was
global and local clustering respectively. Estimated severity of spatial variation using the
partial-sill-to-sill ratio yielded 12.1%, 4.7% and 16.5%, for weight-for-age, height-for-age and weight-for-height Z-scores measures respectively. Maternal death had the greatest negative impact on child HIV/TB mortality. Other determinants included being a male child and belonging to a household that had experienced multiple deaths. A protective effect was found in households with better socio-economic status and where older children were present. Pathway analyses of these factors showed that HIV had a significant mediator effect and the greatest worsening effect on malnutrition after controlling for low birth-weight selection bias Several spatial hot spots of mortality and malnutrition were observed, with these regions consistently emerging as areas of greater risk, which reinforces geographical differentials in these public health indicators.
Conclusion: Modelling that adjusts for spatial random effects, is a potentially useful
technique to disclose hidden patterns. These geographical differences are often ignored in
epidemiological regression modelling resulting in reporting of biased estimates. Proximate
and underlying determinants, notably socioeconomic status and maternal deaths, impacteddirectly and indirectly on child mortality and malnutrition. These factors are highly relevant locally and should be used to formulate interventions to reduce child mortality. Spatial prediction maps can guide policy on where to best target interventions. Child interventions can be more effective if there is a dual focus: treatment and care for those already HIV/TB infected, coupled with prevention in those geographical areas of greatest risk. Public health population-level interventions aimed at reducing child malnutrition are pivotal in lowering morbidity and mortality in remote areas.
Keywords: HIV/TB, Child mortality, Child malnutrition, Conceptual framework, Spatial
analysis, MCMC, Path analysis, South Africa
27
Osman, I. H., A. L. Anouze, N. M. Hindi, Zahir Irani, Habin Lee, and Vishanth J. P. Weerakkody. "I-MEET Framework for the Evaluation eGovernment Services from Engaging Stakeholders' Perspectives." 2014. http://hdl.handle.net/10454/14103.
Full textAbstract:
NoI-MEET is an Integrated Model for Evaluating E-government services Transformation from stakeholders' perspectives. It is based on an integration of concepts from value chain management and business process transformation to optimize the system-wide value chain of providers and users simultaneously. It aims to align stakeholders on a common global value against traditional disintegrated approaches where each stakeholder optimizes its e-service local value at the expense of others. The measured variables are derived from the literature and focused groups. They are then categorized into cost and risk (Inputs) and (benefit and opportunity) Outputs after a validation process based on Structured Equation Models using a sample of 1540 user-responses of e-services in the UK. Finally, Data Envelopment Analysis is conducted to derive an aggregated of an e-service satisfaction value using the various inputs and outputs. The empirical results demonstrate that data-derived weights for aggregating indicators are variable rather than fixed across e-services. The novelty of the assessment approach lies in its capability to provide informed suggestions to set targets to improve an eservice from the perspective of all engaging users. Hence it provides a better transformation of public administration services and improved take up by citizens and businesses.
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Quazi, K. Hassan. "A Framework for Modelling Species-Specific Site Quality Index Based on Data Generated From Remote Sensing Imagery and a Process-Based Model." Thesis, 2008. http://hdl.handle.net/1882/1033.
Full textAbstract:
This Thesis presents a framework for modelling species-specific site quality
index (SQI) at a spatial resolution of 250 m by integrating biophysical variables of
growing degree days (GDD), soil water content (SWC), and incident photosynthetically
active radiation (PAR) in descriptions of potential tree growth. Development of GDD
maps is based on processing and blending remotely-sensed data acquired with the
Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Terra satellite
and ETM+ sensor on Landsat-7 satellite at spatial resolutions of 250 m and 28.5 m.
Descriptions of SWC are based on a temperature-vegetation wetness index (TVWI) that
relies on MODIS-based optical and thermal image products. PAR is estimated with an
existing solar-radiation distribution model. SQI is defined as a function of species vital
attributes and species environmental response to GDD, TVWI, and PAR. The methods
are applied to a balsam fir [bF; Abies balsamea (L.) Mill.] dominated region in
northwest New Brunswick. Comparisons between SQI and field-based estimates of site
index and enhanced vegetation index showed that about 66 and 88% of the values
corresponding to a series of Forest Development Survey lines (691 in total) were within
16% of SQI values. On average 92.1% of high bF-content stands (> 50% composition)
in the area fell on medium-to-very high SQI values (> 0.50). Based on these agreements,
SQI can be perceived as a good predictor of potential tree-species growth in the
selection of optimal sites for biomass and wood fibre production.
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Esteves, Hugo Alexandre Martins. "Predictive analytics applied to Alzheimer’s disease : a data visualisation framework for understanding current research and future challenges." Master's thesis, 2019. http://hdl.handle.net/10362/63807.
Full textAbstract:
Dissertation as a partial requirement for obtaining a master’s degree in information management, with a specialisation in Business Intelligence and Knowledge Management.Big Data is, nowadays, regarded as a tool for improving the healthcare sector in many areas, such as in its economic side, by trying to search for operational efficiency gaps, and in personalised treatment, by selecting the best drug for the patient, for instance. Data science can play a key role in identifying diseases in an early stage, or even when there are no signs of it, track its progress, quickly identify the efficacy of treatments and suggest alternative ones. Therefore, the prevention side of healthcare can be enhanced with the usage of state-of-the-art predictive big data analytics and machine learning methods, integrating the available, complex, heterogeneous, yet sparse, data from multiple sources, towards a better disease and pathology patterns identification. It can be applied for the diagnostic challenging neurodegenerative disorders; the identification of the patterns that trigger those disorders can make possible to identify more risk factors, biomarkers, in every human being. With that, we can improve the effectiveness of the medical interventions, helping people to stay healthy and active for a longer period. In this work, a review of the state of science about predictive big data analytics is done, concerning its application to Alzheimer’s Disease early diagnosis. It is done by searching and summarising the scientific articles published in respectable online sources, putting together all the information that is spread out in the world wide web, with the goal of enhancing knowledge management and collaboration practices about the topic. Furthermore, an interactive data visualisation tool to better manage and identify the scientific articles is develop, delivering, in this way, a holistic visual overview of the developments done in the important field of Alzheimer’s Disease diagnosis.
Big Data é hoje considerada uma ferramenta para melhorar o sector da saúde em muitas áreas, tais como na sua vertente mais económica, tentando encontrar lacunas de eficiência operacional, e no tratamento personalizado, selecionando o melhor medicamento para o paciente, por exemplo. A ciência de dados pode desempenhar um papel fundamental na identificação de doenças em um estágio inicial, ou mesmo quando não há sinais dela, acompanhar o seu progresso, identificar rapidamente a eficácia dos tratamentos indicados ao paciente e sugerir alternativas. Portanto, o lado preventivo dos cuidados de saúde pode ser bastante melhorado com o uso de métodos avançados de análise preditiva com big data e de machine learning, integrando os dados disponíveis, geralmente complexos, heterogéneos e esparsos provenientes de múltiplas fontes, para uma melhor identificação de padrões patológicos e da doença. Estes métodos podem ser aplicados nas doenças neurodegenerativas que ainda são um grande desafio no seu diagnóstico; a identificação dos padrões que desencadeiam esses distúrbios pode possibilitar a identificação de mais fatores de risco, biomarcadores, em todo e qualquer ser humano. Com isso, podemos melhorar a eficácia das intervenções médicas, ajudando as pessoas a permanecerem saudáveis e ativas por um período mais longo. Neste trabalho, é feita uma revisão do estado da arte sobre a análise preditiva com big data, no que diz respeito à sua aplicação ao diagnóstico precoce da Doença de Alzheimer. Isto foi realizado através da pesquisa exaustiva e resumo de um grande número de artigos científicos publicados em fontes online de referência na área, reunindo a informação que está amplamente espalhada na world wide web, com o objetivo de aprimorar a gestão do conhecimento e as práticas de colaboração sobre o tema. Além disso, uma ferramenta interativa de visualização de dados para melhor gerir e identificar os artigos científicos foi desenvolvida, fornecendo, desta forma, uma visão holística dos avanços científico feitos no importante campo do diagnóstico da Doença de Alzheimer.
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Singhal, Vikas. "A conceptual framework for effective BIM-enabled information management in railways." Master's thesis, 2020. http://hdl.handle.net/1822/74958.
Full textAbstract:
Dissertação de mestrado em European Master in Building Information ModellingThe rail-based transportation projects are at the first line of any strategic agenda, as the advantages realized from them such as environmental sustainability, economic benefits and the enhanced transportation safety to the passengers are well researched and proven. It is also important that the methods and processes of BIM are applied to the rail-based projects, for the project executors to deliver the project without time and cost overruns. There are many examples cited to show the advantages that major railway projects around the world have accomplished over the different phases of the project life cycle by embracing the BIM processes. There are impairments in the adoption of BIM across the supply chain in the railway projects, with the huge ecosystem and multiple heterogenous participants. So, it is essential to understand the stakeholder’s arrangements and their requirements in terms of Information, to completely utilize the information management functions using BIM technologies and methods. The integration of the supply chain and appropriate information transition across the project phases are the key considerations for the development of a BIM framework for effective information management across the project. This work aims to contribute to a clarification of the information workflow and the importance of each one of the tools used for its management, throughout the setting up of some conceptual frameworks. Common Data Environment is analyzed as a powerful tool for the management, development, dissemination, and archiving of information, to serve as an integration channel for the supply chain. There are standard recommendations and frameworks developed to assist the organizations for setting up the CDE that will integrate the different stakeholders and act as the Single source of truth of the information for the ecosystem at any given phase of the project. The information requirements are recommended to be divided into Organization Information requirements, Asset Information Requirements, Project Information Requirements and Exchange Information Requirements. It is critical that the requirements are defined based on the requirements of expected BIM Uses, Project Phases essentials and stakeholders’ requirements. The principle of expressing the information requirements at the procurement process is based on ‘keeping end in mind’, which is vital from the operations and maintenance perspectives. The concept of Pre-Contract and Post-Contract BIM Execution Plan are also essential to complete the procurement cycle and the assessment of the delivery team.
Os projetos de transporte ferroviário estão na primeira linha de qualquer agenda estratégica, uma vez que as vantagens que deles resultam, tais como a sustentabilidade ambiental, os benefícios económicos e o aumento da segurança do transporte para os passageiros, estão já bem investigadas e comprovadas. É também importante que os métodos e processos do BIM sejam aplicados aos projetos relacionados com o caminho-de-ferro, para que os executores do projeto o entreguem sem exceder os prazos e os custos. Há já muitos exemplos referenciados, em todo o mundo, para mostrar as vantagens que os grandes projetos ferroviários atingem, ao longo das diversas fases do ciclo de vida do projeto, ao adotarem processos BIM. Existem, porém, deficiências na adoção do BIM ao longo da cadeia de fornecimento, nos projetos ferroviários, dado o enorme ecossistema e múltiplos e heterogéneos participantes. Assim, é essencial compreender as necessidades das partes interessadas e os seus requisitos em termos de Informação, para poder utilizar cabalmente as funções de gestão da informação utilizando as tecnologias e métodos BIM. A integração da cadeia de fornecimento e a transição adequada da informação ao longo das diversas fases do projeto são chave para o completo desenvolvimento de uma estrutura BIM e para uma gestão eficaz da informação em todo o projeto. Este trabalho visa contribuir para uma clarificação do fluxo da informação e da importância de cada uma das ferramentas utilizadas para a sua gestão, através da definição de alguns enquadramentos conceptuais. O Ambiente Comum de Dados é analisado, enquanto ferramenta poderosa para a gestão, desenvolvimento, disseminação e arquivo da informação, para servir como um canal de integração para toda a cadeia de fornecimento. Existem recomendações e enquadramentos normalizados desenvolvidos para apoiar as organizações na definição do CDE de modo a integrar os diferentes intervenientes e atuar como “fonte única da verdade” da informação, para o ecossistema, em qualquer fase do projeto. Recomenda-se que os requisitos de informação sejam divididos em Requisitos de Informação da Organização, Requisitos de Informação de Ativos, Requisitos de Informação do Projeto e Requisitos de Trocas de Informação. É fundamental que os requisitos sejam definidos com base nos requisitos das utilizações esperadas do BIM, das principais fases do projeto e dos requisitos das partes interessadas. O princípio de explicitar os requisitos de informação logo no processo de contratação, baseia-se em "ter o fim em vista", o que se torna vital sob o ponto de vista das operações e manutenção. O conceito de Plano de Execução BIM Pré-Contrato e Pós-Contrato é também essencial para se completar o ciclo de aprovisionamento e a avaliação da equipa fornecedora.