The continued increase in the size and complexity of modern computer systems has led to a commensurate increase in the size of their logs. System logs are an invaluable resource to systems administrators during fault resolution. Fault resolution is a time-consuming and knowledge intensive process. A lot of the time spent in fault resolution is spent sifting through large volumes of information, which includes event logs, to find the root cause of the problem. Therefore, the ability to analyze log files automatically and accurately will lead to significant savings in the time and cost of downtime events for any organization. The automatic analysis and search of system logs for fault symptoms, otherwise called alerts, is the primary motivation for the work carried out in this thesis. The proposed log alert detection scheme is a hybrid framework, which incorporates anomaly detection and signature generation to accomplish its goal. Unlike previous work, minimum apriori knowledge of the system being analyzed is assumed. This assumption enhances the platform portability of the framework. The anomaly detection component works in a bottom-up manner on the contents of historical system log data to detect regions of the log, which contain anomalous (alert) behaviour. The identified anomalous regions are then passed to the signature generation component, which mines them for patterns. Consequently, future occurrences of the underlying alert in the anomalous log region, can be detected on a production system using the discovered pattern. The combination of anomaly detection and signature generation, which is novel when compared to previous work, ensures that a framework which is accurate while still being able to detect new and unknown alerts is attained. Evaluations of the framework involved testing it on log data for High Performance Cluster (HPC), distributed and cloud systems. These systems provide a good range for the types of computer systems used in the real world today. The results indicate that the system that can generate signatures for detecting alerts, which can achieve a Recall rate of approximately 83% and a false positive rate of approximately 0%, on average.

At the present time it is unknown how the U(1)A anomaly of Quantum Chromodynamics behaves at high temperatures. We therefore want to look for thermal changes of the effects of the anomaly. For example, by studying the properties of the η' meson at high temperatures it would be possible to deduce important information on the axial anomaly, thanks to the deep connection between them. In this thesis the width of the η' as a function of the temperature is studied in the framework of large-Nc Chiral Perturbation Theory, at next-to-leading order, and in the corresponding Resonance Chiral Theory. We calculate the width increase due to scattering with particles from the heat bath, which we assume to consist of a pion gas. We compare the results obtained in both frameworks and as expected we find a smaller, but still consistent width increase when the more realistic resonance exchange is taken into account. The results suggest that the in-medium width of the η' may increase up to ΔΓ<img src="http://www.diva-portal.org/cgi-bin/mimetex.cgi?%5Capprox" /> 10 MeV at a temperature of T<img src="http://www.diva-portal.org/cgi-bin/mimetex.cgi?%5Capprox" /> 120 MeV. We find therefore a width increase of considerable size, comparable to the inverse lifetime of the fireball created in relativistic heavy-ion collisions. In other words, our results suggest that it may be possible to study experimentally how the properties of the η' change at high temperatures.

We consider the problem of extracting a specific feature from a noisy signal generated by a multi-channels Remote Field Eddy Current Sensor. The sensor is installed on a mobile robot whose mission is the detection of anomalous regions in metal pipelines. Given the presence of noise that characterizes the data series, anomaly signals could be masked by noise and therefore difficult to identify in some instances. In order to enhance signal peaks that potentially identify anomalies we consider an entropy filter built on a-posteriori probability density functions associated with data series. Thresholds based on the Neyman-Pearson criterion for hypothesis testing are derived. The algorithmic tool is applied to the analysis of data from a portion of pipeline with a set of anomalies introduced at predetermined locations. Critical areas identifying anomalies capture the set of damaged locations, demonstrating the effectiveness of the filter in detection with Remote Field Eddy Current Sensor.

The number of solar panels deployed worldwide has rapidly increased. Solar panels are often placed in areas not easily accessible. It is also difficult for panel owners to be aware of their operating condition. Many environmental factors have negative effects on the efficiency of solar panels. To reduce the power lost caused by environmental factors, it is necessary to detect and classify the anomalous events occurring on the surface of solar panels. This thesis designs and studies a device to continuously measure the voltage output of solar panels and to transmit the time series data back to a personal computer using wireless communication. A program was developed to store and model this time series data. It also detected the existence of anomalies and classified the anomalies by modeling the data. In total, ten types of anomalies were considered. These anomaly types include temporary shading, permanent shading, fallen leaves, accumulating snow and melting snow among others. Previous time series anomaly detection algorithms do not perform well for reallife situations and are only capable of dealing with at most four different types of anomalies. In this work, a general mathematical model is proposed to give better performance in real-life test cases and to cover more than four types of anomalies. We note that the models can be generalized to detect and to classify anomalies for general time series data which is not necessarily generated from solar panel. We compared several techniques to detect and to classify anomalies including the auto-regressive integrated moving average model (ARIMA), neural networks, support vector machines and k-nearest-neighbors classification. We found that anomaly classification using the k-nearest-neighbors classification was able to accurately detect and classify 97% of the anomalies in our test set. The devices and algorithms have been tested with two small 12-volt solar panels.

Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Electrical Engineering, 2008. / Includes bibliographical references.

Thesis (Ph. D.)--Auburn University, 2008. / Abstract. Includes bibliographical references (p. 119-122).

Thesis (Ph. D.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Electrical and Computer Engineering, 2009. / Includes bibliographical references.

In many machine learning problems and application domains, the data are naturally organized by groups. For example, a video sequence is a group of images, an image is a group of patches, a document is a group of paragraphs/words, and a community is a group of people. We call them the collective data. In this thesis, we study how and what we can learn from collective data. Usually, machine learning focuses on individual objects, each of which is described by a feature vector and studied as a point in some metric space. When approaching collective data, researchers often reduce the groups into vectors to which traditional methods can be applied. We, on the other hand, will try to develop machine learning methods that respect the collective nature of data and learn from them directly. Several different approaches were taken to address this learning problem. When the groups consist of unordered discrete data points, it can naturally be characterized by its sufficient statistics – the histogram. For this case we develop efficient methods to address the outliers and temporal effects in the data based on matrix and tensor factorization methods. To learn from groups that contain multi-dimensional real-valued vectors, we develop both generative methods based on hierarchical probabilistic models and discriminative methods using group kernels based on new divergence estimators. With these tools, we can accomplish various tasks such as classification, regression, clustering, anomaly detection, and dimensionality reduction on collective data. We further consider the practical side of the divergence based algorithms. To reduce their time and space requirements, we evaluate and find methods that can effectively reduce the size of the groups with little impact on the accuracy. We also proposed the conditional divergence along with an efficient estimator in order to correct the sampling biases that might be present in the data. Finally, we develop methods to learn in cases where some divergences are missing, caused by either insufficient computational resources or extreme sampling biases. In addition to designing new learning methods, we will use them to help the scientific discovery process. In our collaboration with astronomers and physicists, we see that the new techniques can indeed help scientists make the best of data.

L'utilisation des réseaux de capteurs sans fil (WSN) ne cesse d'augmenter au point de couvrir divers domaines et applications. Cette tendance est supportée par les avancements techniques achevés dans la conception des capteurs, qui ont permis de réduire le coût ainsi que la taille de ces composants. Toutefois, il reste plusieurs défis qui font face au déploiement et au bon fonctionnement de ce type de réseaux et qui parviennent principalement de la limitation des ressources de capteurs ainsi de l'imperfection des données collectées. Dans cette thèse, on adresse le problème de collecte de données et de détection d'anomalies dans les réseaux de capteurs. Nous visons à assurer ces deux fonctionnalités tout en économisant l'utilisation des ressources de capteurs et en prolongeant la durée de vie de réseaux. Tout au long de ce travail, nous présentons plusieurs solutions qui permettent une collecte efficace de données de capteurs ainsi que une bonne détection des éventuelles anomalies. Dans notre première contribution, nous décrivons une solution basée sur la technique Compressive Sensing (CS) qui permet d'équilibrer le trafic transmis par les nœuds dans le réseau. Notre approche diffère des solutions existantes par la prise en compte de la corrélation temporelle ainsi que spatiale dans le processus de décompression des données. De plus, nous proposons une nouvelle formulation pour détecter les anomalies. Les simulations réalisées sur des données réelles prouvent l'efficacité de notre approche en termes de reconstruction de données et de détection d'anomalies par rapport aux approches existantes. Pour mieux optimiser l'utilisation des ressources de WSNs, nous proposons dans une deuxième contribution une solution de collecte de données et de détection d'anomalies basée sur la technique Matrix Completion (MC) qui consiste à transmettre un sous ensemble aléatoire de données de capteurs. Nous développons un algorithme qui estime les mesures manquantes en se basant sur plusieurs propriétés des données. L'algorithme développé permet également de dissimuler les anomalies de la structure normale des données. Cette solution est améliorée davantage dans notre troisième contribution, où nous proposons une formulation différente du problème de collecte de données et de détection d'anomalies. Nous reformulons les connaissances a priori sur les données cibles par des contraintes convexes. Ainsi, les paramètres impliqués dans l'algorithme développé sont liés a certaines propriétés physiques du phénomène observé et sont faciles à ajuster. Nos deux approches montrent de bonnes performances en les simulant sur des données réelles. Enfin, nous proposons dans la dernière contribution une nouvelle technique de collecte de données qui consiste à envoyer que les positions les plus importantes dans la représentation parcimonieuse des données uniquement. Nous considérons dans cette approche le bruit qui peut s'additionner aux données reçues par le nœud collecteur. Cette solution permet aussi de détecter les pics dans les mesures prélevées. En outre, nous validons l'efficacité de notre solution par une analyse théorique corroborée par des simulations sur des données réelles / The use of Wireless Sensor Networks (WSN)s is steadily increasing to cover various applications and domains. This trend is supported by the technical advancements in sensor manufacturing process which allow a considerable reduction in the cost and size of these components. However, there are several challenges facing the deployment and the good functioning of this type of networks. Indeed, WSN's applications have to deal with the limited energy, memory and processing capacities of sensor nodes as well as the imperfection of the probed data. This dissertation addresses the problem of collecting data and detecting anomalies in WSNs. The aforementioned functionality needs to be achieved while ensuring a reliable data quality at the collector node, a good anomaly detection accuracy, a low false alarm rate as well as an efficient energy consumption solution. Throughout this work, we provide different solutions that allow to meet these requirements. Foremost, we propose a Compressive Sensing (CS) based solution that allows to equilibrate the traffic carried by nodes regardless their distance from the sink. This solution promotes a larger lifespan of the WSN since it balances the energy consumption between sensor nodes. Our approach differs from existing CS-based solutions by taking into account the sparsity of sensory representation in the temporal domain in addition to the spatial dimension. Moreover, we propose a new formulation to detect aberrant readings. The simulations carried on real datasets prove the efficiency of our approach in terms of data recovering and anomaly detection compared to existing solutions. Aiming to further optimize the use of WSN resources, we propose in our second contribution a Matrix Completion (MC) based data gathering and anomaly detection solution where an arbitrary subset of nodes contributes at the data gathering process at each operating period. To fill the missing values, we mainly relay on the low rank structure of sensory data as well as the sparsity of readings in some transform domain. The developed algorithm also allows to dissemble anomalies from the normal data structure. This solution is enhanced in our third contribution where we propose a constrained formulation of the data gathering and anomalies detection problem. We reformulate the textit{a prior} knowledge about the target data as hard convex constraints. Thus, the involved parameters into the developed algorithm become easy to adjust since they are related to some physical properties of the treated data. Both MC based approaches are tested on real datasets and demonstrate good capabilities in terms of data reconstruction quality and anomaly detection performance. Finally, we propose in the last contribution a position based compressive data gathering scheme where nodes cooperate to compute and transmit only the relevant positions of their sensory sparse representation. This technique provide an efficient tool to deal with the noisy nature of WSN environment as well as detecting spikes in the sensory data. Furthermore, we validate the efficiency of our solution by a theoretical analysis and corroborate it by a simulation evaluation

In this thesis we consider concentration inequalities and the concentration of measure phenomenon from a variety of angles. Sharp tail bounds on the deviation of Lipschitz functions of independent random variables about their mean are well known. We consider variations on this theme for dependent variables on the Boolean cube. In recent years negatively associated probability distributions have been studied as potential generalizations of independent random variables. Results on this class of distributions have been sparse at best, even when restricting to the Boolean cube. We consider the class of negatively associated distributions topologically, as a subset of the general class of probability measures. Both the weak (distributional) topology and the total variation topology are considered, and the simpler notion of negative correlation is investigated. The concentration of measure phenomenon began with Milman's proof of Dvoretzky's theorem, and is therefore intimately connected to the field of high-dimensional convex geometry. Recently this field has found application in the area of compressed sensing. We consider these applications and in particular analyze the use of Gordon's min-max inequality in various compressed sensing frameworks, including the Dantzig selector and the matrix uncertainty selector. Finally we consider the use of concentration inequalities in developing a theoretically sound anomaly detection algorithm. Our method uses a ranking procedure based on KNN graphs of given data. We develop a max-margin learning-to-rank framework to train limited complexity models to imitate these KNN scores. The resulting anomaly detector is shown to be asymptotically optimal in that for any false alarm rate α, its decision region converges to the α-percentile minimum volume level set of the unknown underlying density.