Search results for "Model"

showing 10 items of 24058 documents

Collaborative EA Information Elicitation Method : The IEM for Business Architecture

2015

This study contributes to the enterprise architecture (EA) methodologies by suggesting a method for eliciting architecture requirements: gathering both the current architecture information, and the development needs and requirements for the business architecture (BA) dimension in EA planning. Most of all EA dimensions, the developing of the BA requires collaboration with various non-IT stakeholders. It presents thus challenges to the IT department, or the consultancy involved in EA related efforts. The contribution of the various stakeholder groups as informants is, however, crucial to well founded EA design decisions. The suggested method takes related IS development fields as starting poi…

ta113EngineeringKnowledge managementRequirements engineeringbusiness.industryrequirements elicitationComputingMethodologies_MISCELLANEOUSStakeholderEnterprise architectureInformation technologyRequirements elicitationpublic administrationBusiness process modelingKnowledge acquisitionmethodsmenetelmätenterprise architectureBusiness architecturejulkinen hallintokokonaisarkkitehtuuribusiness
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The Quest for Underpinning Theory of Enterprise Architecture - General Systems Theory

2017

Enterprise architecture originates from the 1980’s. It emerged among ICT practitioners to solve complex problems related to information systems. Currently EA is also utilised to solve business problems, although the focus is still in ICT and its alignment with business. EA can be defined as a description of the current and future states of the enterprise, and as a change between these states to meet stakeholder’s goals. Despite its popularity and 30 years of age, the literature review conducted on top information and management science journals revealed that EA is still lacking the sound theoretical foundation. In this conceptual paper, we propose General Systems Theory (GST) for underpinni…

ta113Enterprise architecture frameworkUnderpinningComputer science05 social sciencesEnterprise architecture02 engineering and technologygeneral systems theoryManagementEngineering managementSystems theoryEnterprise architecture management020204 information systemsenterprise architecture0502 economics and businessBusiness architecture0202 electrical engineering electronic engineering information engineeringkokonaisarkkitehtuuriView model050203 business & managementProceedings of the 19th International Conference on Enterprise Information Systems
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How and Why to Start and Run a SIGCHI Local Chapter

2015

There is a vast and increasing interest towards local HCI communities around the globe and in particular on geographical areas in which HCI has only recently started to gain increasing interest by local industries as well as academic institutions. A SIGCHI Local Chapter is one of the ways a local HCI community can organize and get visibility and support for their activities. However, many active volunteers in this field might not be aware of this possibility. The main goal of the Chapters' SIG in CHI'15 is to inform interested parties of SIGCHI Local Chapters and to find ways in which SIGCHI could better support local HCI communities with their various needs all over the world.

ta113Human-Computer Interaction (HCI)ComputingMilieux_THECOMPUTINGPROFESSIONMultimediabusiness.industryField (Bourdieu)local communitiesVisibility (geometry)GlobePublic relationsSpecial Interest Groupcomputer.software_genreLocal communityspecial interest groupsInformationSystems_MODELSANDPRINCIPLESmedicine.anatomical_structurePolitical sciencemedicineInformationSystems_MISCELLANEOUSbusinesscomputerProceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems
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Interface Detection Using a Quenched-Noise Version of the Edwards-Wilkinson Equation

2015

We report here a multipurpose dynamic-interface-based segmentation tool, suitable for segmenting planar, cylindrical, and spherical surfaces in 3D. The method is fast enough to be used conveniently even for large images. Its implementation is straightforward and can be easily realized in many environments. Its memory consumption is low, and the set of parameters is small and easy to understand. The method is based on the Edwards-Wilkinson equation, which is traditionally used to model the equilibrium fluctuations of a propagating interface under the influence of temporally and spatially varying noise. We report here an adaptation of this equation into multidimensional image segmentation, an…

ta113Image segmentationta114DiscretizationInterface (Java)Computer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONobject detectionimage edge detectionImage segmentationComputer Graphics and Computer-Aided DesignGrayscaleGray-scaleObject detectionSurface topographyNoiseMathematical modelThree-dimensional displaysSegmentationTomography3D image processingNoiseSurface morphologyAlgorithmSoftwareIEEE Transactions on Image Processing
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Reduced Order Models for Pricing European and American Options under Stochastic Volatility and Jump-Diffusion Models

2017

Abstract European options can be priced by solving parabolic partial(-integro) differential equations under stochastic volatility and jump-diffusion models like the Heston, Merton, and Bates models. American option prices can be obtained by solving linear complementary problems (LCPs) with the same operators. A finite difference discretization leads to a so-called full order model (FOM). Reduced order models (ROMs) are derived employing proper orthogonal decomposition (POD). The early exercise constraint of American options is enforced by a penalty on subset of grid points. The presented numerical experiments demonstrate that pricing with ROMs can be orders of magnitude faster within a give…

ta113Mathematical optimizationGeneral Computer ScienceStochastic volatilityDifferential equationEuropean optionMonte Carlo methods for option pricingJump diffusion010103 numerical & computational mathematics01 natural sciencesTheoretical Computer Science010101 applied mathematicsValuation of optionsModeling and Simulationlinear complementary problemRange (statistics)Asian optionreduced order modelFinite difference methods for option pricing0101 mathematicsAmerican optionoption pricingMathematicsJournal of Computational Science
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Interactive Multiple Criteria Decision Making based on preference driven Evolutionary Multiobjective Optimization with controllable accuracy

2012

Abstract We present an approach to interactive Multiple Criteria Decision Making based on preference driven Evolutionary Multiobjective Optimization with controllable accuracy. The approach relies on formulae for lower and upper bounds on coordinates of the outcome of an arbitrary efficient variant corresponding to preference information expressed by the Decision Maker. In contrast to earlier works on that subject, here lower and upper bounds can be calculated and their accuracy controlled entirely within evolutionary computation framework. This is made possible by exploration of not only the region of feasible variants – a standard within evolutionary optimization, but also the region of i…

ta113Mathematical optimizationInformation Systems and ManagementGeneral Computer ScienceComputationta111Contrast (statistics)Interactive evolutionary computationManagement Science and Operations ResearchMulti-objective optimizationOutcome (game theory)Industrial and Manufacturing EngineeringEvolutionary computationModeling and SimulationPreference (economics)Evolutionary programmingMathematicsEuropean Journal of Operational Research
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Reduced Order Models for Pricing American Options under Stochastic Volatility and Jump-diffusion Models

2016

American options can be priced by solving linear complementary problems (LCPs) with parabolic partial(-integro) differential operators under stochastic volatility and jump-diffusion models like Heston, Merton, and Bates models. These operators are discretized using finite difference methods leading to a so-called full order model (FOM). Here reduced order models (ROMs) are derived employing proper orthogonal decomposition (POD) and non negative matrix factorization (NNMF) in order to make pricing much faster within a given model parameter variation range. The numerical experiments demonstrate orders of magnitude faster pricing with ROMs. peerReviewed

ta113Mathematical optimizationStochastic volatilityDiscretizationComputer scienceJump diffusionFinite difference method010103 numerical & computational mathematics01 natural sciencesNon-negative matrix factorization010101 applied mathematicsValuation of optionslinear complementary problemRange (statistics)General Earth and Planetary SciencesApplied mathematicsreduced order modelFinite difference methods for option pricing0101 mathematicsAmerican optionoption pricingGeneral Environmental ScienceProcedia Computer Science
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Iterative Methods for Pricing American Options under the Bates Model

2013

We consider the numerical pricing of American options under the Bates model which adds log-normally distributed jumps for the asset value to the Heston stochastic volatility model. A linear complementarity problem (LCP) is formulated where partial derivatives are discretized using finite differences and the integral resulting from the jumps is evaluated using simple quadrature. A rapidly converging fixed point iteration is described for the LCP, where each iterate requires the solution of an LCP. These are easily solved using a projected algebraic multigrid (PAMG) method. The numerical experiments demonstrate the efficiency of the proposed approach. Furthermore, they show that the PAMG meth…

ta113Mathematical optimizationStochastic volatilityDiscretizationIterative methodComputer scienceFinite difference methodLinear complementarity problemIterative methodQuadrature (mathematics)Multigrid methodFixed-point iterationBates modelLinear complementarity problemGeneral Earth and Planetary SciencesPartial derivativeAmerican optionGeneral Environmental ScienceProcedia Computer Science
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A New Augmented Lagrangian Approach for $L^1$-mean Curvature Image Denoising

2015

Variational methods are commonly used to solve noise removal problems. In this paper, we present an augmented Lagrangian-based approach that uses a discrete form of the L1-norm of the mean curvature of the graph of the image as a regularizer, discretization being achieved via a finite element method. When a particular alternating direction method of multipliers is applied to the solution of the resulting saddle-point problem, this solution reduces to an iterative sequential solution of four subproblems. These subproblems are solved using Newton’s method, the conjugate gradient method, and a partial solution variant of the cyclic reduction method. The approach considered here differs from ex…

ta113Mean curvatureDiscretizationimage denoisingAugmented Lagrangian methodApplied MathematicsGeneral Mathematicsmean curvaturekuvankäsittelyTopologyFinite element methodimage processingsymbols.namesakeLagrangian relaxationLagrange multiplierConjugate gradient methodsymbolsApplied mathematicsaugmented Lagrangian methodalternating direction methods of multipliersvariational modelMathematicsCyclic reductionSIAM Journal on Imaging Sciences
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Evaluating the performance of artificial neural networks for the classification of freshwater benthic macroinvertebrates

2014

Abstract Macroinvertebrates form an important functional component of aquatic ecosystems. Their ability to indicate various types of anthropogenic stressors is widely recognized which has made them an integral component of freshwater biomonitoring. The use of macroinvertebrates in biomonitoring is dependent on manual taxa identification which is currently a time-consuming and cost-intensive process conducted by highly trained taxonomical experts. Automated taxa identification of macroinvertebrates is a relatively recent research development. Previous studies have displayed great potential for solutions to this demanding data mining application. In this research we have a collection of 1350 …

ta113Radial basis function networkEcologyArtificial neural networkComputer sciencebusiness.industryApplied MathematicsEcological Modelingta1172PerceptronMachine learningcomputer.software_genreBackpropagationComputer Science ApplicationsProbabilistic neural networkIdentification (information)Computational Theory and MathematicsModeling and SimulationMultilayer perceptronConjugate gradient methodta1181Artificial intelligencebusinesscomputerEcology Evolution Behavior and SystematicsEcological Informatics
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