Academic literature on the topic 'Data modelling frameworks'
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Journal articles on the topic "Data modelling frameworks"
Murray, S. G., C. Power, and A. S. G. Robotham. "Modelling Galaxy Populations in the Era of Big Data." Proceedings of the International Astronomical Union 10, S306 (May 2014): 304–6. http://dx.doi.org/10.1017/s1743921314010710.
Full textUrquhart, Christine, and Dina Tbaishat. "Reflections on the value and impact of library and information services." Performance Measurement and Metrics 17, no. 1 (April 11, 2016): 29–44. http://dx.doi.org/10.1108/pmm-01-2016-0004.
Full textHerath, Herath Mudiyanselage Viraj Vidura, Jayashree Chadalawada, and Vladan Babovic. "Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling." Hydrology and Earth System Sciences 25, no. 8 (August 11, 2021): 4373–401. http://dx.doi.org/10.5194/hess-25-4373-2021.
Full textStøa, Bente, Rune Halvorsen, Sabrina Mazzoni, and Vladimir I. Gusarov. "Sampling bias in presence-only data used for species distribution modelling: theory and methods for detecting sample bias and its effects on models." Sommerfeltia 38, no. 1 (October 1, 2018): 1–53. http://dx.doi.org/10.2478/som-2018-0001.
Full textOden, J. Tinsley. "Adaptive multiscale predictive modelling." Acta Numerica 27 (May 1, 2018): 353–450. http://dx.doi.org/10.1017/s096249291800003x.
Full textChartier, Jean-François, Davide Pulizzotto, Louis Chartrand, and Jean-Guy Meunier. "A data-driven computational semiotics: The semantic vector space of Magritte’s artworks." Semiotica 2019, no. 230 (October 25, 2019): 19–69. http://dx.doi.org/10.1515/sem-2018-0120.
Full textShivakumar, Abhishek, Thomas Alfstad, and Taco Niet. "A clustering approach to improve spatial representation in water-energy-food models." Environmental Research Letters 16, no. 11 (October 29, 2021): 114027. http://dx.doi.org/10.1088/1748-9326/ac2ce9.
Full textaus der Beek, T., M. Flörke, D. M. Lapola, R. Schaldach, F. Voß, and E. Teichert. "Modelling historical and current irrigation water demand on the continental scale: Europe." Advances in Geosciences 27 (September 7, 2010): 79–85. http://dx.doi.org/10.5194/adgeo-27-79-2010.
Full textWilbert, Niko, Tiziano Zito, Rike-Benjamin Schuppner, Zbigniew Jędrzejewski-Szmek, Laurenz Wiskott, and Pietro Berkes. "Building extensible frameworks for data processing: The case of MDP, Modular toolkit for Data Processing." Journal of Computational Science 4, no. 5 (September 2013): 345–51. http://dx.doi.org/10.1016/j.jocs.2011.10.005.
Full textLedien, Julia, Zulma M. Cucunubá, Gabriel Parra-Henao, Eliana Rodríguez-Monguí, Andrew P. Dobson, Susana B. Adamo, María-Gloria Basáñez, and Pierre Nouvellet. "Linear and machine learning modelling for spatiotemporal disease predictions: Force-of-infection of Chagas disease." PLOS Neglected Tropical Diseases 16, no. 7 (July 19, 2022): e0010594. http://dx.doi.org/10.1371/journal.pntd.0010594.
Full textDissertations / Theses on the topic "Data modelling frameworks"
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.
Full textHempel, 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.
Full textSerpeka, 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.
Full textSilverwood, 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/.
Full textEkaterina, Guseva. "The Conceptual Integration Modelling Framework: Semantics and Query Answering." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/33464.
Full textMgbemena, Chidozie Simon. "A data-driven framework for investigating customer retention." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13175.
Full textMouline, 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.
Full textSelf-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
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.
Full textDuong, 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.
Full textDuong, 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.
Full textMost 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.
Books on the topic "Data modelling frameworks"
Vanrolleghem, Peter A. Modelling aspects of water framework directive implementation. London: IWA Pub., 2010.
Find full textStefan, Förster. A formal framework for modelling component extension and layers in distributed embedded systems. Dresden: TUDpress, 2007.
Find full textRamackers, Guustaaf Jan. Integrated object modelling: An executable specification framework for business analysis and information system design. Amsterdam: Thesis Publishers, 1994.
Find full text1968-, Lawry Jonathan, Shanahan James G, and Ralescu Anca L. 1949-, eds. Modelling with words: Learning, fusion, and reasoning within a formal linguistic representation framework. Berlin: Springer, 2003.
Find full textVanrolleghem, Peter A. Modelling Aspects of Water Framework Directive Implementation. IWA Publishing, 2010.
Find full textBianconi, Ginestra. Multilayer Network Models. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0010.
Full textAbdullah, Ahmad Fikri Bin. Methodology for Processing Raw LIDAR Data to Support Urban Flood Modelling Framework: UNESCO-IHE PhD Thesis. Taylor & Francis Group, 2012.
Find full textAbdullah, Ahmad Fikri Bin. Methodology for Processing Raw LIDAR Data to Support Urban Flood Modelling Framework: UNESCO-IHE PhD Thesis. Taylor & Francis Group, 2020.
Find full textAbdullah, Ahmad Fikri Bin. Methodology for Processing Raw LIDAR Data to Support Urban Flood Modelling Framework: UNESCO-IHE PhD Thesis. Taylor & Francis Group, 2020.
Find full textAbdullah, Ahmad Fikri Bin. Methodology for Processing Raw LIDAR Data to Support Urban Flood Modelling Framework: UNESCO-IHE PhD Thesis. Taylor & Francis Group, 2020.
Find full textBook chapters on the topic "Data modelling frameworks"
Farsi, Maryam, Amin Hosseinian-Far, Alireza Daneshkhah, and Tabassom Sedighi. "Mathematical and Computational Modelling Frameworks for Integrated Sustainability Assessment (ISA)." In Strategic Engineering for Cloud Computing and Big Data Analytics, 3–27. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52491-7_1.
Full textLéonard, Michel, and Ian Prince. "NelleN: A framework for literate data modelling." In Notes on Numerical Fluid Mechanics and Multidisciplinary Design, 239–56. Cham: Springer International Publishing, 1992. http://dx.doi.org/10.1007/bfb0035135.
Full textDegiannakis, Stavros, and Christos Floros. "Multiple Model Comparison and Hypothesis Framework Construction." In Modelling and Forecasting High Frequency Financial Data, 110–60. London: Palgrave Macmillan UK, 2015. http://dx.doi.org/10.1057/9781137396495_4.
Full textChen, Yenming J., and Albert Jing-Fuh Yang. "Crowd Density Estimation from Few Radio-Frequency Tracking Devices: I. A Modelling Framework." In Data Mining and Big Data, 390–98. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61845-6_39.
Full textZieliński, Bartosz. "Modular Term-Rewriting Framework for Artifact-Centric Business Process Modelling." In Model and Data Engineering, 71–78. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66854-3_6.
Full textDíaz Mercado, Vitali. "Methodological framework." In Spatio-Temporal Characterisation of Drought: Data Analytics, Modelling, Tracking, Impact and Prediction, 23–28. London: CRC Press, 2022. http://dx.doi.org/10.1201/9781003279655-3.
Full textQin, Zengchang, and Jonathan Lawry. "Knowledge Discovery in a Framework for Modelling with Words." In Soft Computing for Knowledge Discovery and Data Mining, 241–76. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-69935-6_11.
Full textKiran, Deshpande, and Madhuri Rao. "Modelling Auto-scalable Big Data Enabled Log Analytic Framework." In Computer Networks and Inventive Communication Technologies, 857–70. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3035-5_64.
Full textHari Prasad, D., and M. Punithavalli. "An Integrated Framework for Mixed Data Clustering Using Growing Hierarchical Self-Organizing Map (GHSOM)." In Mathematical Modelling and Scientific Computation, 471–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28926-2_53.
Full textOthman, Muhaini, Siti Aisyah Mohamed, Mohd Hafizul Afifi Abdullah, Munirah Mohd Yusof, and Rozlini Mohamed. "A Framework to Cluster Temporal Data Using Personalised Modelling Approach." In Advances in Intelligent Systems and Computing, 181–90. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72550-5_18.
Full textConference papers on the topic "Data modelling frameworks"
Bamba, Inshita, Yashika, Jahanvi Singh, Pronika Chawla, and Kritika Soni. "Big Social Data and Modelling Frameworks." In 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). IEEE, 2021. http://dx.doi.org/10.1109/icais50930.2021.9395935.
Full textLi, Yaqiong, Xuhui Fan, Ling Chen, Bin Li, Zheng Yu, and Scott A. Sisson. "Recurrent Dirichlet Belief Networks for interpretable Dynamic Relational Data Modelling." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/342.
Full textBerryman, Matthew, Rohan Wickramasuriya, Vu Lam Co, Qun Chen, and Pascal Pascal. "Modelling and Data Frameworks for Understanding Infrastructure Systems through a Systems-of-Systems Lens." In International Symposium for Next Generation Infrastructure. University of Wollongong, SMART Infrastructure Facility, 2014. http://dx.doi.org/10.14453/isngi2013.proc.5.
Full textLener, Alberto. "Foundational Study of Artificial Intelligence Reservoir Simulation by Integrating Digital Core Technology and Logging Data to Optimise Recovery." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211066-ms.
Full textHonfi, Dániel, John Leander, Ivar Björnsson, and Oskar Larsson Ivanov. "A practical approach for supporting decisions in bridge condition assessment and monitoring." In IABSE Congress, New York, New York 2019: The Evolving Metropolis. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2019. http://dx.doi.org/10.2749/newyork.2019.2136.
Full textBohlmann, Sebastian, Matthias Becker, Helena Szczerbicka, and Volkhard Klinger. "A Data Management Framework Providing Online-Connectivity In Symbiotic Simulation." In 24th European Conference on Modelling and Simulation. ECMS, 2010. http://dx.doi.org/10.7148/2010-0302-0308.
Full textKoffi, Itoro Udofort. "A Deep Learning Approach for the Prediction of Oil Formation Volume Factor." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/208627-stu.
Full textSchallenberg, A., W. Nebel, A. Herrholz, P. A. Hartmann, and F. Oppenheimer. "OSSS+R: A framework for application level modelling and synthesis of reconfigurable systems." In 2009 Design, Automation & Test in Europe Conference & Exhibition (DATE'09). IEEE, 2009. http://dx.doi.org/10.1109/date.2009.5090805.
Full text"Enhancement of water storage estimates using GRACE data assimilation with particle filter framework." In 22nd International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2017. http://dx.doi.org/10.36334/modsim.2017.h5.tangdamrongsub.
Full textRubio-Solis, Adrian, George Panoutsos, and Steve Thornton. "A Data-driven fuzzy modelling framework for the classification of imbalanced data." In 2016 IEEE 8th International Conference on Intelligent Systems (IS). IEEE, 2016. http://dx.doi.org/10.1109/is.2016.7737438.
Full textReports on the topic "Data modelling frameworks"
Russell, H. A. J., and S. K. Frey. Canada One Water: integrated groundwater-surface-water-climate modelling for climate change adaptation. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329092.
Full textFaverjon, Céline, Angus Cameron, and Marco De Nardi. Modelling framework to quantify the risk of AMR exposure via food products - example of chicken and lettuce. Food Standards Agency, April 2022. http://dx.doi.org/10.46756/sci.fsa.qum110.
Full textDaudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe, and Hamid Mehmood. Mapping WASH-related disease risk: A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, December 2021. http://dx.doi.org/10.53328/uxuo4751.
Full textNechaev, V., Володимир Миколайович Соловйов, and A. Nagibas. Complex economic systems structural organization modelling. Politecnico di Torino, 2006. http://dx.doi.org/10.31812/0564/1118.
Full textMurad, M. Hassan, Stephanie M. Chang, Celia Fiordalisi, Jennifer S. Lin, Timothy J. Wilt, Amy Tsou, Brian Leas, et al. Improving the Utility of Evidence Synthesis for Decision Makers in the Face of Insufficient Evidence. Agency for Healthcare Research and Quality (AHRQ), April 2021. http://dx.doi.org/10.23970/ahrqepcwhitepaperimproving.
Full textSett, Dominic, Florian Waldschmidt, Alvaro Rojas-Ferreira, Saut Sagala, Teresa Arce Mojica, Preeti Koirala, Patrick Sanady, et al. Climate and disaster risk analytics tool for adaptive social protection. United Nations University - Institute for Environment and Human Security, March 2022. http://dx.doi.org/10.53324/wnsg2302.
Full textVerburg, Peter H., Žiga Malek, Sean P. Goodwin, and Cecilia Zagaria. The Integrated Economic-Environmental Modeling (IEEM) Platform: IEEM Platform Technical Guides: User Guide for the IEEM-enhanced Land Use Land Cover Change Model Dyna-CLUE. Inter-American Development Bank, September 2021. http://dx.doi.org/10.18235/0003625.
Full textDownes, Jane, ed. Chalcolithic and Bronze Age Scotland: ScARF Panel Report. Society for Antiquaries of Scotland, September 2012. http://dx.doi.org/10.9750/scarf.09.2012.184.
Full textRankin, Nicole, Deborah McGregor, Candice Donnelly, Bethany Van Dort, Richard De Abreu Lourenco, Anne Cust, and Emily Stone. Lung cancer screening using low-dose computed tomography for high risk populations: Investigating effectiveness and screening program implementation considerations: An Evidence Check rapid review brokered by the Sax Institute (www.saxinstitute.org.au) for the Cancer Institute NSW. The Sax Institute, October 2019. http://dx.doi.org/10.57022/clzt5093.
Full textCOLD FORMED STEEL SHEAR WALL RACKING ANALYSIS THROUGH A MECHANISTIC APPROACH: CFS-RAMA. The Hong Kong Institute of Steel Construction, September 2022. http://dx.doi.org/10.18057/ijasc.2022.18.3.2.
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