A framework for the analysis and evaluation of enterprise models

Bibliography: leaves 264-288.The purpose of this study is the development and validation of a comprehensive framework for the analysis and evaluation of enterprise models. The study starts with an extensive literature review of modelling concepts and an overview of the various reference disciplines concerned with enterprise modelling. This overview is more extensive than usual in order to accommodate readers from different backgrounds. The proposed framework is based on the distinction between the syntactic, semantic and pragmatic model aspects and populated with evaluation criteria drawn from an extensive literature survey. In order to operationalize and empirically validate the framework, an exhaustive survey of enterprise models was conducted. From this survey, an XML database of more than twenty relatively large, publicly available enterprise models was constructed. A strong emphasis was placed on the interdisciplinary nature of this database and models were drawn from ontology research, linguistics, analysis patterns as well as the traditional fields of data modelling, data warehousing and enterprise systems. The resultant database forms the test bed for the detailed framework-based analysis and its public availability should constitute a useful contribution to the modelling research community. The bulk of the research is dedicated to implementing and validating specific analysis techniques to quantify the various model evaluation criteria of the framework. The aim for each of the analysis techniques is that it can, where possible, be automated and generalised to other modelling domains. The syntactic measures and analysis techniques originate largely from the disciplines of systems engineering, graph theory and computer science. Various metrics to measure model hierarchy, architecture and complexity are tested and discussed. It is found that many are not particularly useful or valid for enterprise models. Hence some new measures are proposed to assist with model visualization and an original "model signature" consisting of three key metrics is proposed.Perhaps the most significant contribution ofthe research lies in the development and validation of a significant number of semantic analysis techniques, drawing heavily on current developments in lexicography, linguistics and ontology research. Some novel and interesting techniques are proposed to measure, inter alia, domain coverage, model genericity, quality of documentation, perspicuity and model similarity. Especially model similarity is explored in depth by means of various similarity and clustering algorithms as well as ways to visualize the similarity between models. Finally, a number of pragmatic analyses techniques are applied to the models. These include face validity, degree of use, authority of model author, availability, cost, flexibility, adaptability, model currency, maturity and degree of support. This analysis relies mostly on the searching for and ranking of certain specific information details, often involving a degree of subjective interpretation, although more specific quantitative procedures are suggested for some of the criteria. To aid future researchers, a separate chapter lists some promising analysis techniques that were investigated but found to be problematic from methodological perspective. More interestingly, this chapter also presents a very strong conceptual case on how the proposed framework and the analysis techniques associated vrith its various criteria can be applied to many other information systems research areas. The case is presented on the grounds of the underlying isomorphism between the various research areas and illustrated by suggesting the application of the framework to evaluate web sites, algorithms, software applications, programming languages, system development methodologies and user interfaces

Optimization of epitaxial graphene growth for quantum metrology

The electrical quantum standards have played a decisive role in modern metrology, particularly since the introduction of the revised International System of Units (SI) in May 2019. By adapting the basic units to exactly defined natural constants, the quantized Hall resistance (QHR) standards are also given precisely. The Von Klitzing constant RK = h/e2 (h Planck's constant and e elementary charge) can be measured precisely using the quantum Hall effect (QHE) and is thus the primary representation of the ohm. Currently, the QHR standard based on GaAs/AlGaAs heterostructure has succeeded in yielding robust resistance measurements with high accuracy <10−9. In recent years, graphene has been vastly investigated due to its potential in QHR metrology. This single-layer hexagonal carbon crystal forms a two-dimensional electron gas system and exhibits the QHE, due to its properties, even at higher temperatures. Thereby, in the future the QHR standards could be realized in more simplified experimental conditions that can be used at higher temperatures and currents as well as smaller magnetic fields than is feasible in conventional GaAs/AlGaAs QHR. The quality of the graphene is of significant importance to the QHR standards application. The epitaxial graphene growth on silicon carbide (SiC) offers decisive advantages among the known fabrication methods. It enables the production of large-area graphene layers that are already electron-doped and do not have to be transferred to another substrate. However, there are fundamental challenges in epitaxial graphene growth. During the high-temperature growth process, the steps on the SiC surface bunch together and form terraces with high steps. This so-called step-bunching gives rise to the graphene thickness inhomogeneity (e.g., the bilayer formation) and extrinsic resistance anisotropy, which both deteriorate the performance of electronic devices made from it. In this thesis, the process conditions of the epitaxial graphene growth through a so-called polymer-assisted sublimation growth method are minutely investigated. Atomic force microscopy (AFM) is used to show that the previously neglected flow-rate of the argon process gas has a significant influence on the morphology of the SiC substrate and atop carbon layers. The results can be well explained using a simple model for the thermodynamic conditions at the layer adjacent to the surface. The resulting control option of step-bunching on the sub-nanometer scales is used to produce the ultra-flat, monolayer graphene layers without the bilayer inclusions that exhibit the vanishing of the resistance anisotropy. The comparison of four-point and scanning tunneling potentiometry measurements shows that the remaining small anisotropy represents the ultimate limit, which is given solely by the remaining resistances at the SiC terrace steps. Thanks to the advanced growth control, also large-area homogenous quasi-freestanding monolayer and bilayer graphene sheets are fabricated. The Raman spectroscopy and scanning tunneling microscopy reveal very low defect densities of the layers. In addition, the excellent quality of the produced freestanding layers is further evidenced by the four-point measurement showing low extrinsic resistance anisotropy in both micro- and millimeter-scales. The precise control of step-bunching using the Ar flow also enables the preparation of periodic non-identical SiC surfaces under the graphene layer. Based on the work function measurements by Kelvin-Probe force microscopy and X-ray photoemission electron microscopy, it is shown for the first time that there is a doping variation in graphene, induced by a proximity effect of the different near-surface SiC stacks. The comparison of the AFM and low-energy electron microscopy measurements have enabled the exact assignment of the SiC stacks, and the examinations have led to an improved understanding of the surface restructuring in the framework of a step-flow model. The knowledge gained can be further utilized to improve the performance of epitaxial graphene quantum resistance standard, and overall, the graphene-based electronic devices. Finally, the QHR measurements have been shown on the optimized graphene monolayers. In order to operate the graphene-based QHR at desirably low magnetic field ranges (B < 5 T), two known charge tuning techniques are applied, and the results are discussed with a view to their further implementation in the QHR metrology. Keywords: Quantum resistance metrology, epitaxial graphene growth, silicon carbide, resistance anisotropy, argon flow-rate, homogenous quasi-freestanding grapheneElektrische Quantennormale spielen eine wichtige Rolle in der modernen Metrologie, besonders seit der Einführung des revidierten Einheitensystems (SI) im Mai 2019. Durch die Zurückführung der Basiseinheiten auf exakt definierte Naturkonstanten sind auch die quantisierten Werte von Widerstandsnormalen (QHR) exakt gegeben. Die Von-Klitzing-Konstante RK = h/e2 (h Planck-Konstante und e Elementarladung) lässt sich mittels des Quanten-Hall-Effekts (QHE) präzise messen und ist somit die primäre Darstellung des Ohm. Die Quanten-Widerstandsnormale bestehen aktuell aus robusten GaAs/AlGaAs-Heterostrukturen, die eine Genauigkeit <10−9 für die Widerstands-Messung erlauben. In den letzten Jahren wird verstärkt Graphen auf sein Potenzial für die Widerstandmetrologie untersucht. Der einlagige hexagonale Kohlenstoffkristall bildet ebenfalls ein zweidimensionales Elektrongas aus, das den Quanten-Hall-Effekt zeigt – und dies auf Grund seiner Eigenschaften schon bei höheren Temperaturen. Damit könnten in Zukunft Widerstandsnormale für vereinfachte experimentelle Bedingungen realisiert werden, die bei höheren Temperaturen und Strömen oder kleineren Magnetfeldern eingesetzt werden können, als es mit konventionellen GaAs/AlGaAs- QHR möglich ist. Für den Einsatz als Widerstandsnormal ist die Qualität des Graphens von entscheidender Bedeutung. Unter den bekannten Herstellungsmethoden bietet das epitaktische Wachstum von Graphen auf Siliciumcarbid (SiC) entscheidende Vorteile. Es lassen sich damit großflächige Graphenschichten herstellen, die nicht auf ein anderes Substrat übertragen werden müssen. Allerdings gibt es grundlegende Herausforderungen beim epitaktischen Wachstum. So tritt bei hohen Prozesstemperaturen eine Bündelung der Kristallstufen auf der SiC-Substratoberfläche auf (Step-bunching), was zu einer bekannten extrinsischen Widerstandsanisotropie führt und darüber hinaus die Bildung von Bilagen-Graphen begünstigt. Beides verschlechtert die Eigenschaften der daraus hergestellten Widerstandsnormale. In dieser Dissertation werden zunächst die Prozessbedingungen des mittels der sogenannten Polymer-Assisted-Sublimations-Growth-Methode hergestellten epitaktischen Graphens auf SiC genauer untersucht. Mithilfe der Rasterkraft-Mikroskopie (Atomic-Force-Microscopy, AFM) wird gezeigt, dass es einen erheblichen Einfluss der bisher wenig beachteten Flussrate des Prozessgases Argon auf die Morphologie des SiC-Substrates und der oberen Kohlenstoffschichten gibt. Anhand eines einfachen Modells für die thermodynamischen Verhältnisse in einer oberflächennahen Schicht lassen sich die Ergebnisse hervorragend erklären. Die sich daraus ergebende Kontrollmöglichkeit des Step-bunching auf Sub-Nanometer-Skalen wird genutzt, um ultraflache, monolagige Graphenschichten ohne Bilageneinschlüsse herzustellen, die eine verschwindende Widerstandsanisotropie aufweisen. Der Vergleich von Vierpunkt-Messungen und Scanning-Tunneling-Potentiometery-Messungen zeigt, dass die verbleibende geringe Anisotropie das ultimative Limit darstellt, die allein durch die verbleibenden Widerstände an den SiC-Terrassenstufen gegeben ist. Dank der fortschrittlichen Wachstumskontrolle werden auch großflächige, homogene quasi-freistehende Monolage- und Bilage-Graphenschichten hergestellt. Die Raman-Spektroskopie und die Rastertunnel-Mikroskopie zeigen sehr geringe Defektdichten der Schichten. Darüber hinaus wird die hervorragende Qualität der hergestellten quasi-freistehenden Schichten durch die Vierpunkt-Messung unter Beweis gestellt, die eine geringe extrinsische Widerstandsanisotropie zeigt. Die präzise Kontrolle des Step-bunching mittels Ar-Fluss ermöglicht auch die gezielte Präparation von periodischen, nicht-identischen SiC-Oberflächen unter der Graphenlage. Anhand von Messungen der Austrittsarbeit mit Kelvin-Probe-Force-Microscopy und X-ray Photoemission-Electron-Microscopy konnte erstmals gezeigt werden, dass es eine Variation der Graphendotierung, induziert durch einen Proximity Effekt der unterschiedlichen oberflächennahen SiC-Stapel, gibt. Der Vergleich von AFM und Low-Energy-Electron-Microscopy-Messungen ermöglicht die genaue Zuordnung der SiC-Stapel und die Untersuchungen führen insgesamt zu einem verbesserten Verständnis der Oberflächen-Umstrukturierung im Rahmen eines adäquaten Step-Flow-Modells. Die gesammelten Erkenntnisse können zur Verbesserung der Eigenschaften von Graphen-Quantennormalen und auch allgemein von graphenbasierten Bauteilen genutzt werden. Abschließend werden QH-Widerstandsmessungen an optimierten Graphen-Monolagen gezeigt. Um den Magnetfeldbereich (B < 5 T) einzuschränken, werden zwei bekannte extrinsische Dotiertechniken verwendet und die Ergebnisse werden im Hinblick auf den weiteren Einsatz in der QH-Metrologie diskutiert. Schlüsselwörter: Wachstum des epitaktischen Graphens, Siliciumcarbid, Argon-Flussrate, Widerstandsanisotropie, homogenes quasi-freistehendes Graphe

Discriminative, generative, and imitative learning

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2002.Includes bibliographical references (leaves 201-212).I propose a common framework that combines three different paradigms in machine learning: generative, discriminative and imitative learning. A generative probabilistic distribution is a principled way to model many machine learning and machine perception problems. Therein, one provides domain specific knowledge in terms of structure and parameter priors over the joint space of variables. Bayesian networks and Bayesian statistics provide a rich and flexible language for specifying this knowledge and subsequently refining it with data and observations. The final result is a distribution that is a good generator of novel exemplars. Conversely, discriminative algorithms adjust a possibly non-distributional model to data optimizing for a specific task, such as classification or prediction. This typically leads to superior performance yet compromises the flexibility of generative modeling. I present Maximum Entropy Discrimination (MED) as a framework to combine both discriminative estimation and generative probability densities. Calculations involve distributions over parameters, margins, and priors and are provably and uniquely solvable for the exponential family. Extensions include regression, feature selection, and transduction. SVMs are also naturally subsumed and can be augmented with, for example, feature selection, to obtain substantial improvements. To extend to mixtures of exponential families, I derive a discriminative variant of the Expectation-Maximization (EM) algorithm for latent discriminative learning (or latent MED).(cont.) While EM and Jensen lower bound log-likelihood, a dual upper bound is made possible via a novel reverse-Jensen inequality. The variational upper bound on latent log-likelihood has the same form as EM bounds, is computable efficiently and is globally guaranteed. It permits powerful discriminative learning with the wide range of contemporary probabilistic mixture models (mixtures of Gaussians, mixtures of multinomials and hidden Markov models). We provide empirical results on standardized data sets that demonstrate the viability of the hybrid discriminative-generative approaches of MED and reverse-Jensen bounds over state of the art discriminative techniques or generative approaches. Subsequently, imitative learning is presented as another variation on generative modeling which also learns from exemplars from an observed data source. However, the distinction is that the generative model is an agent that is interacting in a much more complex surrounding external world. It is not efficient to model the aggregate space in a generative setting. I demonstrate that imitative learning (under appropriate conditions) can be adequately addressed as a discriminative prediction task which outperforms the usual generative approach. This discriminative-imitative learning approach is applied with a generative perceptual system to synthesize a real-time agent that learns to engage in social interactive behavior.by Tony Jebara.Ph.D