L'importance des systèmes collaboratifs a considérablement augmenté au cours des dernières années. La majorité de nouvelles applications sont conçues de manière distribuée pour répondre aux besoins du travail collaboratif. Parmi ces applications, nous nous intéressons aux éditeurs collaboratifs temps-réel (RCE) qui permettent la manipulation de divers objets partagés, tels que les pages wiki ou les articles scientifiques par plusieurs personnes réparties dans le temps et dans l'espace. Bien que ces applications sont de plus en plus utilisées dans de nombreux domaines, l'absence d'un modèle de contrôle d'accès adéquat limite l'exploitation de leur plein potentiel. En effet, contrôler les accès aux documents partagés de façon décentralisée et sans alourdir les performances du système collaboratif représente un vrai challenge, surtout que les droits d'accès peuvent changer fréquemment et de façon dynamique au cours du temps. Dans cette thèse, nous proposons un modèle de contrôle d'accès générique basé sur l'approche de réplication optimiste du document partagé ainsi que sa politique de contrôle d'accès. Pour cela, nous proposons une approche optimiste de contrôle d'accès dans la mesure où un utilisateur peut violer temporairement la politique de sécurité. Pour assurer la convergence, nous faisons recours à l'annulation sélective pour éliminer l'effet des mises à jour illégales. Vu l'absence d'une solution d'annulation générique et correcte, nous proposons une étude théorique du problème d'annulation et nous concevons une solution générique basée sur une nouvelle sémantique de l'opération identité. Afin de valider notre approche tous nos algorithmes ont été implémentés en Java et testés sur la plateforme distribuée Grid'5000 / The importance of collaborative systems in real-world applications has grown significantly over the recent years. The majority of new applications are designed in a distributed fashion to meet collaborative work requirements. Among these applications, we focus on Real-Time Collaborative Editors (RCE) that provide computer support for modifying simultaneously shared documents, such as articles, wiki pages and programming source code by dispersed users. Although such applications are more and more used into many fields, the lack of an adequate access control concept is still limiting their full potential. In fact, controlling access in a decentralized fashion for such systems is a challenging problem, as they need dynamic access changes and low latency access to shared documents. In this thesis, we propose a generic access control model based on replicating the shared document and its authorization policy at the local memory of each user. We consider the propagation of authorizations and their interactions. We propose a optimistic approach to enforce access control in existing collaborative editing solutions in the sense that a user can temporarily violate the access control policy. To enforce the policy, we resort to the selective undo approach in order to eliminate the effect of illegal document updates. Since, the safe undo is an open issue in collaborative applications. We investigate a theoretical study of the undo problem and propose a generic solution for selectively undoing operations. Finally, we apply our framework on a collaboration prototype and measure its performance in the distributed grid GRID?5000 to highlight the scalability of our solution

<p>This thesis concerns the identifiability issue; which, if any, parameters can be deduced from the input and output behavior of a model? The two types of identifiability concepts, a priori and practical, will be addressed and explained. Two methods for identifiability testing are evaluated and the result shows that the two methods work well if they are combined. The first method is for a priori identifiability analysis and it can determine the a priori identifiability of a system in polynomial time. The result from the method is probabilistic with a high probability of correct answer. The other method takes the simulation approach to determine whether the model is practically identifiable. Non-identifiable parameters manifest themselves as a functional relationship between the parameters and the method uses transformations of the parameter estimates to conclude if the parameters are linked. The two methods are verified on models with known identifiability properties and then tested on some examples from systems biology. Although the output from one of the methods is cumbersome to interpret, the results show that the number of parameters that can be determined in practice (practical identifiability) are far fewer than the ones that can be determined in theory (a priori identifiability). The reason for this is the lack of quality, noise and lack of excitation, of the measurements.</p> / <p>Fokus i denna rapport är på identifierbarhetsproblemet. Vilka parametrar kan unikt bestämmas från en modell? Det existerar två typer av identifierbarhetsbegrepp, a priori och praktisk identifierbarhet, som kommer att förklaras. Två metoder för identifierbarhetstestning är utvärderade och resultaten visar på att de två metoderna fungerar bra om de kombineras med varandra. Den första metoden är för a priori identifierbarhetsanalys och den kan avgöra identifierbarheten för ett system i polynomiell tid. Resultaten från metoden är slumpmässigt med hög sannolikhet för ett korrekt svar. Den andra metoden använder sig av simuleringar för att avgöra om modellen är praktiskt identifierbar. Icke-identifierbara parametrar yttrar sig som funktionella kopplingar mellan parametrar och metoden använder sig av transformationer av parameterskattningarna för att avgöra om parametrarna är kopplade. De två metoderna är verifierade på modeller där identifierbarheten är känd och är därefter testade på några exempel från systembiologi. Trots att resultaten från den ena metoden är besvärliga att tolka visar resultaten på att antalet parametrar som går att bestämma i verkligheten (praktiskt identifierbara) är betydligt färre än de parametrar som kan bestämmas i teorin (a priori identifierbara). Anledningen beror på brist på kvalitet, både brus och brist på excitation, i mätningarna.</p>

This thesis concerns the identifiability issue; which, if any, parameters can be deduced from the input and output behavior of a model? The two types of identifiability concepts, a priori and practical, will be addressed and explained. Two methods for identifiability testing are evaluated and the result shows that the two methods work well if they are combined. The first method is for a priori identifiability analysis and it can determine the a priori identifiability of a system in polynomial time. The result from the method is probabilistic with a high probability of correct answer. The other method takes the simulation approach to determine whether the model is practically identifiable. Non-identifiable parameters manifest themselves as a functional relationship between the parameters and the method uses transformations of the parameter estimates to conclude if the parameters are linked. The two methods are verified on models with known identifiability properties and then tested on some examples from systems biology. Although the output from one of the methods is cumbersome to interpret, the results show that the number of parameters that can be determined in practice (practical identifiability) are far fewer than the ones that can be determined in theory (a priori identifiability). The reason for this is the lack of quality, noise and lack of excitation, of the measurements. / Fokus i denna rapport är på identifierbarhetsproblemet. Vilka parametrar kan unikt bestämmas från en modell? Det existerar två typer av identifierbarhetsbegrepp, a priori och praktisk identifierbarhet, som kommer att förklaras. Två metoder för identifierbarhetstestning är utvärderade och resultaten visar på att de två metoderna fungerar bra om de kombineras med varandra. Den första metoden är för a priori identifierbarhetsanalys och den kan avgöra identifierbarheten för ett system i polynomiell tid. Resultaten från metoden är slumpmässigt med hög sannolikhet för ett korrekt svar. Den andra metoden använder sig av simuleringar för att avgöra om modellen är praktiskt identifierbar. Icke-identifierbara parametrar yttrar sig som funktionella kopplingar mellan parametrar och metoden använder sig av transformationer av parameterskattningarna för att avgöra om parametrarna är kopplade. De två metoderna är verifierade på modeller där identifierbarheten är känd och är därefter testade på några exempel från systembiologi. Trots att resultaten från den ena metoden är besvärliga att tolka visar resultaten på att antalet parametrar som går att bestämma i verkligheten (praktiskt identifierbara) är betydligt färre än de parametrar som kan bestämmas i teorin (a priori identifierbara). Anledningen beror på brist på kvalitet, både brus och brist på excitation, i mätningarna.

In today's competitive business and industrial environment, it is becoming more crucial than ever to assess precisely process losses due to non-compliance to customer specifications. To assess these losses, industry is extensively using Process Capability Indices for performance evaluation of their processes. Determination of the performance capability of a stable process using the standard process capability indices such as and requires that the underlying quality characteristics data follow a normal distribution. However it is an undisputed fact that real processes very often produce non-normal quality characteristics data and also these quality characteristics are very often correlated with each other. For such non-normal and correlated multivariate quality characteristics, application of standard capability measures using conventional methods can lead to erroneous results. The research undertaken in this PhD thesis presents several capability assessment methods to estimate more precisely and accurately process performances based on univariate as well as multivariate quality characteristics. The proposed capability assessment methods also take into account the correlation, variance and covariance as well as non-normality issues of the quality characteristics data. A comprehensive review of the existing univariate and multivariate PCI estimations have been provided. We have proposed fitting Burr XII distributions to continuous positively skewed data. The proportion of nonconformance (PNC) for process measurements is then obtained by using Burr XII distribution, rather than through the traditional practice of fitting different distributions to real data. Maximum likelihood method is deployed to improve the accuracy of PCI based on Burr XII distribution. Different numerical methods such as Evolutionary and Simulated Annealing algorithms are deployed to estimate parameters of the fitted Burr XII distribution. We have also introduced new transformation method called Best Root Transformation approach to transform non-normal data to normal data and then apply the traditional PCI method to estimate the proportion of non-conforming data. Another approach which has been introduced in this thesis is to deploy Burr XII cumulative density function for PCI estimation using Cumulative Density Function technique. The proposed approach is in contrast to the approach adopted in the research literature i.e. use of best-fitting density function from known distributions to non-normal data for PCI estimation. The proposed CDF technique has also been extended to estimate process capability for bivariate non-normal quality characteristics data. A new multivariate capability index based on the Generalized Covariance Distance (GCD) is proposed. This novel approach reduces the dimension of multivariate data by transforming correlated variables into univariate ones through a metric function. This approach evaluates process capability for correlated non-normal multivariate quality characteristics. Unlike the Geometric Distance approach, GCD approach takes into account the scaling effect of the variance-covariance matrix and produces a Covariance Distance variable that is based on the Mahanalobis distance. Another novelty introduced in this research is to approximate the distribution of these distances by a Burr XII distribution and then estimate its parameters using numerical search algorithm. It is demonstrates that the proportion of nonconformance (PNC) using proposed method is very close to the actual PNC value.

In this thesis, we develop some inferential results for one-shot device testing data analysis. These extend and generalize existing methods in the literature. First, a competing-risk model is introduced for one-shot testing data under accelerated life-tests. One-shot devices are products which will be destroyed immediately after use. Therefore, we can observe only a binary status as data, success or failure, of such products instead of its lifetime. Many one-shot devices contain multiple components and failure of any one of them will lead to the failure of the device. Failed devices are inspected to identify the specific cause of failure. Since the exact lifetime is not observed, EM algorithm becomes a natural tool to obtain the maximum likelihood estimates of the model parameters. Here, we develop the EM algorithm for competing exponential and Weibull cases. Second, a semi-parametric approach is developed for simple one-shot device testing data. Semi-parametric estimation is a model that consists of parametric and non-parametric components. For this purpose, we only assume the hazards at different stress levels are proportional to each other, but no distributional assumption is made on the lifetimes. This provides a greater flexibility in model fitting and enables us to examine the relationship between the reliability of devices and the stress factors. Third, Bayesian inference is developed for one-shot device testing data under exponential distribution and Weibull distribution with non-constant shape parameters for competing risks. Bayesian framework provides statistical inference from another perspective. It assumes the model parameters to be random and then improves the inference by incorporating expert's experience as prior information. This method is shown to be very useful if we have limited failure observation wherein the maximum likelihood estimator may not exist. The thesis proceeds as follows. In Chapter 2, we assume the one-shot devices to have two components with lifetimes having exponential distributions with multiple stress factors. We then develop an EM algorithm for developing likelihood inference for the model parameters as well as some useful reliability characteristics. In Chapter 3, we generalize to the situation when lifetimes follow a Weibull distribution with non-constant shape parameters. In Chapter 4, we propose a semi-parametric model for simple one-shot device test data based on proportional hazards model and develop associated inferential results. In Chapter 5, we consider the competing risk model with exponential lifetimes and develop inference by adopting the Bayesian approach. In Chapter 6, we generalize these results on Bayesian inference to the situation when the lifetimes have a Weibull distribution. Finally, we provide some concluding remarks and indicate some future research directions in Chapter 7. / Thesis / Doctor of Philosophy (PhD)