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Loggar är en viktig del av alla system, det ger en inblick i vad som sker. Att analysera loggar och extrahera väsentlig information är en av de största trenderna nu inom IT-branchen. Informationen i loggar är värdefulla resurser som kan användas för att upptäcka anomalier och hantera dessa innan det drabbar användaren. I detta examensarbete dyker vi in i grunderna för informationssökning och analysera undantagsutskrifter i loggar från COSMIC för att undersöka om det är möjligt att upptäcka anomalier med hjälp av retrospektivdata. Detta examensarbete ger även en inblick i möjligheten att visualisera data från loggar och erbjuda en kraftfull sökmotor. Därför kommer vi att fördjupa oss i de tre välkända program som adresserar frågorna i centraliserad loggning: Elasticsearch, Logstash och Kibana. Sammanfattningsvis visar resultatet att det är möjligt att upptäckta anomalier genom att tillämpa statistiska metoder både på retrospektiv- och realtidsdata. / Logs are an important part of any system; it provides an insight into what is happening. One of the biggest trends in the IT industry is analyzing logs and extracting essential information. The information in the logs are valuable resources that can be used to detect anomalies and manage them before it affects the user In this thesis we will dive into the basics of the information retrieval and analyze exceptions in the logs from COSMIC to investigate whether it is feasible to detect anomalies using retrospective data. This thesis also gives an insight into whether it’s possible to visualize data from logs and offer a powerful search engine. Therefore we will dive into the three well known applications that addresses the issues in centralized logging: Elasticsearch, Logstash and Kibana. In summary, our results shows that it’s possible to detected anomalies by applying statistical methods on both in retrospective and real time data.

<p>Deep ‘Analog Artificial Neural Networks’ (AANNs) perform complex classification problems with high accuracy. However, they rely on humongous amount of power to perform the calculations, veiling the accuracy benefits. The biological brain on the other hand is significantly more powerful than such networks and consumes orders of magnitude less power, indicating some conceptual mismatch. Given that the biological neurons are locally connected, communicate using energy efficient trains of spikes, and the behavior is non-deterministic, incorporating these effects in Artificial Neural Networks (ANNs) may drive us few steps towards a more realistic neural networks. </p> <p> </p> <p>Emerging devices can offer a plethora of benefits including power efficiency, faster operation, low area in a vast array of applications. For example, memristors and Magnetic Tunnel Junctions (MTJs) are suitable for high density, non-volatile Random Access Memories when compared with CMOS implementations. In this work, we analyze the possibility of harnessing the characteristics of such emerging devices, to achieve neuro-inspired solutions to intricate problems.</p> <p> </p> <p>We propose how the inherent stochasticity of nano-scale resistive devices can be utilized to realize the functionality of spiking neurons and synapses that can be incorporated in deep stochastic Spiking Neural Networks (SNN) for image classification problems. While ANNs mainly dwell in the aforementioned classification problem solving domain, they can be adapted for a variety of other applications. One such neuro-inspired solution is the Cellular Neural Network (CNN) based Boolean satisfiability solver. Boolean satisfiability (k-SAT) is an NP-complete (k≥3) problem that constitute one of the hardest classes of constraint satisfaction problems. We provide a proof of concept hardware based analog k-SAT solver that is built using MTJs. The inherent physics of MTJs, enhanced by device level modifications, is harnessed here to emulate the intricate dynamics of an analog, CNN based, satisfiability (SAT) solver. </p> <p> </p> <p>Furthermore, in the effort of reaching human level performance in terms of accuracy, increasing the complexity and size of ANNs is crucial. Efficient algorithms for evaluating neural network performance is of significant importance to improve the scalability of networks, in addition to designing hardware accelerators. We propose a scalable approach for evaluating Liquid State Machines: a bio-inspired computing model where the inputs are sparsely connected to a randomly interlinked reservoir (or liquid). It has been shown that biological neurons are more likely to be connected to other neurons in the close proximity, and tend to be disconnected as the neurons are spatially far apart. Inspired by this, we propose a group of locally connected neuron reservoirs, or an ensemble of liquids approach, for LSMs. We analyze how the segmentation of a single large liquid to create an ensemble of multiple smaller liquids affects the latency and accuracy of an LSM. In our analysis, we quantify the ability of the proposed ensemble approach to provide an improved representation of the input using the Separation Property (SP) and Approximation Property (AP). Our results illustrate that the ensemble approach enhances class discrimination (quantified as the ratio between the SP and AP), leading to improved accuracy in speech and image recognition tasks, when compared to a single large liquid. Furthermore, we obtain performance benefits in terms of improved inference time and reduced memory requirements, due to lower number of connections and the freedom to parallelize the liquid evaluation process.</p>

Die Reprogrammierung somatischer Zellen hat sich kürlich als leistungsfähige Technik für die Herstellung von induzierten pluripotenten Stammzellen (iPS Zellen) aus terminal differenzierten Zellen bewährt. Trotz der großen Hoffnung, die sie speziell im Bezug auf patientenspezifische Stammzelltherapie darstellt, gibt es viele Hindernisse auf dem Weg zur Anwendung in der Humanmedizin, die sich von niedrigen Effizienzen bei der technischen Umsetzung bis hin zur unerwünschten Integration von Onkogenen in das menschliche Genom erstrecken. Aus diesem Grund ist es unabdingbar, unser Verständnis der zugrundeliegenden Prozesse und Mechanismen zu vertiefen. Durch neue Datengewinnungsmethoden und stetig wachsende biologische Komplexität hat sich der Denkansatz der Systembiologie in den letzten Jahrzehnten stark etabliert und erfährt eine fortwährende Entwicklung seiner Anwendbarkeit auf komplexe biologische und biochemische Zusammenhänge. Verschiedene mathematische Modellierungsmethoden werden auf den Reprogrammierungsprozess angewendet um Engpässe und mögliche Effizienz-Optimierungen zu erforschen. Es werden topologische Merkmale eines Pluripotenznetzwerkes untersucht, um Unterschiede zu zufällig generierten Netzen und so topologische Einschränkungen des biologisch relevanten Netzwerkes zu finden. Die Optimierung eines Booleschen Modells aus einem selbst kuratierten Netzwerk in Bezug auf Genexpressionsdaten aus Reprogrammierungsexperimenten gewährt tiefgreifende Einblicke in die ersten Schritte und wichtigsten Faktoren des Prozesses. Der Transkriptionsfaktor SP1 spielt hierbei eine wichtige Rolle zur Induktion eines intermediären, transkriptionell inaktiven Zustands. Ein probabilistisches Boole''sches Modell verdeutlicht das Zusammenspiel epigenetischer und transkriptioneller Kontrollprozesse zusammen, um Pluripotenz- und Zelllinien-Entscheidungen in Reprogrammierung und Differenzierung zu treffen. Erklärungen für die geringe Effizienz werden versucht. / Somatic Cell Reprogramming has emerged as a powerful technique for the generation of induced pluripotent stem cells (iPSCs) from terminally differentiated cells in recent years. Although holding great promises for future clinical development, especially in patient specific stem cell therapy, the barriers on the way to a human application are manifold ranging from low technical efficiencies to undesirable integration of oncogenes into the genome. It is thus indispensable to further our understanding of the underlying processes involved in this technique. With the advent of new data acquisition technologies and an ever-growing complexity of biological knowledge, the Systems Biology approach has seen an evolution of its applicability to the elaborate questions and problems of researchers. Using different mathematical modeling approaches the process of somatic cell reprogramming is examined to find out bottlenecks and possible enhancements of its efficiency. I analyze the topological characteristics of a pluripotency network in order to find differences to randomly generated networks and thus deduce constraints of the biologically relevant network. The optimization of a Boolean model from a curated network against early reprogramming gene expression profiles reveals profound insights into the first steps and most important factors of the process. The transcription factor SP1 emerges to play an important role in the induction of an intermediate, transcriptionally inactive state. A probabilistic Boolean network (PBN) illustrates the interplay of transcriptional and epigenetic regulatory processes in order to explain pluripotency and cell lineage decisions in reprogramming and differentiation. Explanations for the low reprogramming efficiencies are tried.

Η παρούσα διατριβή πραγματεύεται το αντικείμενο της μάθησης από δύο διαφορετικές οπτικές γωνίες: την επαγωγική και την παραγωγική μάθηση. Αρχικά, παρουσιάζονται παραμετροποιήσεις στοχαστικών μεθόδων εξόρυξης γνώσης από δεδομένα υπό τη μορφή τεσσάρων καινοτόμων εξατομικευμένων μοντέλων στήριξης ασθενών που πάσχουν από διαταραχές άγχους. Τα τρία μοντέλα προσανατολίζονται στην ανεύρεση πιθανών συσχετίσεων μεταξύ των περιβαλλοντικών παραμέτρων των ασθενών και του επιπέδου άγχους που αυτοί παρουσιάζουν, ενώ παράλληλα προτείνεται και η χρήση ενός Μπεϋζιανού μοντέλου πρόβλεψης του επιπέδου άγχους που είναι πιθανό να εμφανίσει κάποιος ασθενής δεδομένων ορισμένων τιμών του περιβαλλοντικού του πλαισίου εφαρμογής. Αναφορικά με το χώρο της εξόρυξης γνώσης από κείμενο και του μετασχηματισμού συμβολοσειρών, προτείνεται η εκπαίδευση μοντέλων δέντρων αποφάσεων για την αυτόματη μεταγραφή Ελληνικού κειμένου στην αντίστοιχη φωνητική του αναπαράσταση, πραγματοποιείται η στοχαστική μοντελοποίηση όλων των πιθανών μεταγραφικών νορμών από ορθογραφημένα Ελληνικά σε Greeklish και τέλος παρουσιάζεται ένας καινοτόμος αλγόριθμος που συνδυάζει δύο γνωστά για την ικανοποιητική τους απόδοση μέτρα σύγκρισης ομοιότητας αλφαριθμητικών προκειμένου να επιτευχθεί η αυτόματη λημματοποίηση του κειμένου εισόδου. Επιπρόσθετα, στα πλαίσια της ανάπτυξης συστημάτων που θα διευκολύνουν την ανάκτηση εγγράφων ή πληροφοριών προτείνεται η συνδυαστική χρήση του προαναφερθέντος αλγορίθμου λημματοποίησης παράλληλα με τη χρήση ενός πιθανοτικού δικτύου Bayes στοχεύοντας στην ανάπτυξη ενός εύρωστου και ανταγωνιστικού ως προς τις επιδόσεις συστήματος ανάκτησης πληροφοριών. Τέλος, παρουσιάζονται οι προτάσεις μας που αφορούν στο χώρο της παραγωγικής μάθησης και του ελέγχου ικανοποιησιμότητας λογικών εκφράσεων. Συγκεκριμένα περιλαμβάνουν: i) την ανάλυση και εκτενή παρουσίαση μιας καινοτόμας μαθηματικής μοντελοποίησης με την ονομασία AnaLog (Analytic Tableaux Logic) η οποία δύναται να εκφράσει τη λογική που διέπει τους αναλυτικούς πίνακες για προτασιακούς τύπους σε κανονική διαζευκτική μορφή. Mέσω του λογισμού Analog επιτυγχάνεται η εύρεση των κλειστών κλάδων του πλήρως ανεπτυγμένου δέντρου Smullyan, χωρίς να είναι απαραίτητος ο αναλυτικός σχεδιασμός του δέντρου, και ii) την παράθεση ενός αναλυτικού αλγορίθμου που μπορεί να αξιοποιήσει τον φορμαλισμό AnaLog σε ένα πλαίσιο αριθμητικής διαστημάτων μέσω του οποίου μπορούμε να αποφανθούμε για την ικανοποιησιμότητα συμβατικών διαζευκτικών προτασιακών εκφράσεων. / The present dissertation deals with the problem of learning from two different perspectives, meaning the inferential and the deductive learning. Initially, we present our suggestions regarding the parameterization of stochastic data mining methods in the form of four treatment supportive services for patients suffering from anxiety disorders. Three of these services focus on the discovery of possible associations between the patients’ contextual data whereas the last one aims at predicting the stress level a patient might suffer from, in a given environmental context. Our proposals with regards to the wider area of text mining and string conversion include: i) the employment of decision-tree based models for the automatic conversion of Greek texts into their equivalent CPA format, ii) the stochastic modeling of all the existing transliteration norms for the Greek to Greeklish conversion in the form of a robust transcriber and iii) a novel algorithm that is able to combine two well-known for their satisfactory performance string distance metric models in order to address the problem of automatic word lemmatization. With regards to the development of systems that would facilitate the automatic information retrieval, we propose the employment of the aforementioned lemmatization algorithm in order to reduce the ambiguity posed by the plethora of morphological variations of the processed language along with the parallel use of probabilistic Bayesian Networks aiming at the development of a robust and competitive modern information retrieval system. Finally, our proposals regarding logical deduction and satisfiability checking include: i) a novel mathematical formalism of the analytic tableaux methodology named AnaLog (after the terms Analytic Tableaux Logic) which allows us to efficiently simulate the structure and the properties of a complete clausal tableau given an input CNF formula. Via the AnaLog calculus it is made possible to calculate all the closed branches of the equivalent complete Smullyan tree without imposing the need to fully construct it, and ii) a practical application of the AnaLog calculus within an interval arithmetic framework which is able to decide upon the satisfiability of propositional formulas in CNF format. This framework, apart from constituting an illustrative demonstration of the application of the AnaLog calculus, it may also be employed as an alternative conventional SAT system.

In this thesis, I investigate dynamic and computational properties of prokaryotic signalling architectures commonly known as the Two Component Signalling networks and phosphorelays. The aim of this study is to understand the information processing capabilities of different prokaryotic signalling architectures by examining the dynamics they exhibit. I present original investigations into the dynamics of different phosphorelay architectures and identify network architectures that include a commonly found four step phosphorelay architecture with a capacity for tuning its steady state output to implement different signal-response behaviours viz. sigmoidal and hyperbolic response. Biologically, this tuning can be implemented through physiological processes like regulating total protein concentrations (e.g. via transcriptional regulation or feedback), altering reaction rate constants through binding of auxiliary proteins on relay components, or by regulating bi-functional activity in relays which are mediated by bifunctional histidine kinases. This study explores the importance of different biochemical arrangements of signalling networks and their corresponding response dynamics. Following investigations into the significance of various biochemical reactions and topological variants of a four step relay architecture, I explore the effects of having different types of proteins in signalling networks. I show how multi-domain proteins in a phosphorelay architecture with multiple phosphotransfer steps occurring on the same protein can exhibit multistability through a combination of double negative and positive feedback loops. I derive a minimal multistable (core) architecture and show how component sharing amongst networks containing this multistable core can implement computational logic (like AND, OR and ADDER functions) that allows cells to integrate multiple inputs and compute an appropriate response. I examine the genomic distribution of single and multi domain kinases and annotate their partner response regulator proteins across prokaryotic genomes to find the biological significance of dynamics that these networks embed and the processes they regulate in a cell. I extract data from a prokaryotic two component protein database and take a sequence based functional annotation approach to identify the process, function and localisation of different response regulators as signalling partners in these networks. In summary, work presented in this thesis explores the dynamic and computational properties of different prokaryotic signalling networks and uses them to draw an insight into the biological significance of multidomain sensor kinases in living cells. The thesis concludes with a discussion on how this understanding of the dynamic and computational properties of prokaryotic signalling networks can be used to design synthetic circuits involving different proteins comprising two component and phosphorelay architectures.

Dans cette thèse, nous nous sommes intéressés aux problèmes d'ordonnancement et d'optimisation dans des contextes probabilistes. Les contributions de cette thèse se déclinent en deux parties. La première partie est dédiée à l’optimisation de différents mécanismes de tolérance aux pannes pour les machines de très large échelle qui sont sujettes à une probabilité de pannes. La seconde partie est consacrée à l’optimisation du coût d’exécution des arbres d’opérateurs booléens sur des flux de données.Dans la première partie, nous nous sommes intéressés aux problèmes de résilience pour les machines de future génération dites « exascales » (plateformes pouvant effectuer 1018 opérations par secondes).Dans le premier chapitre, nous présentons l’état de l’art des mécanismes les plus utilisés dans la tolérance aux pannes et des résultats généraux liés à la résilience.Dans le second chapitre, nous étudions un modèle d’évaluation des protocoles de sauvegarde de points de reprise (checkpoints) et de redémarrage. Le modèle proposé est suffisamment générique pour contenir les situations extrêmes: d’un côté le checkpoint coordonné, et de l’autre toute une famille de stratégies non-Coordonnées. Nous avons proposé une analyse détaillée de plusieurs scénarios, incluant certaines des plateformes de calcul existantes les plus puissantes, ainsi que des anticipations sur les futures plateformes exascales.Dans les troisième, quatrième et cinquième chapitres, nous étudions l'utilisation conjointe de différents mécanismes de tolérance aux pannes (réplication, prédiction de pannes et détection d'erreurs silencieuses) avec le mécanisme traditionnel de checkpoints et de redémarrage. Nous avons évalué plusieurs modèles au moyen de simulations. Nos résultats montrent que ces modèles sont bénéfiques pour un ensemble de modèles d'applications dans le cadre des futures plateformes exascales.Dans la seconde partie de la thèse, nous étudions le problème de la minimisation du coût de récupération des données par des applications lors du traitement d’une requête exprimée sous forme d'arbres d'opérateurs booléens appliqués à des prédicats sur des flux de données de senseurs. Le problème est de déterminer l'ordre dans lequel les prédicats doivent être évalués afin de minimiser l'espérance du coût du traitement de la requête. Dans le sixième chapitre, nous présentons l'état de l'art de la seconde partie et dans le septième chapitre, nous étudions le problème pour les requêtes exprimées sous forme normale disjonctive. Nous considérons le cas plus général où chaque flux peut apparaître dans plusieurs prédicats et nous étudions deux modèles, le modèle où chaque prédicat peut accéder à un seul flux et le modèle où chaque prédicat peut accéder à plusieurs flux. / In this thesis, we are interested in scheduling and optimization problems in probabilistic contexts. The contributions of this thesis come in two parts. The first part is dedicated to the optimization of different fault-Tolerance mechanisms for very large scale machines that are subject to a probability of failure and the second part is devoted to the optimization of the expected sensor data acquisition cost when evaluating a query expressed as a tree of disjunctive Boolean operators applied to Boolean predicates. In the first chapter, we present the related work of the first part and then we introduce some new general results that are useful for resilience on exascale systems.In the second chapter, we study a unified model for several well-Known checkpoint/restart protocols. The proposed model is generic enough to encompass both extremes of the checkpoint/restart space, from coordinated approaches to a variety of uncoordinated checkpoint strategies. We propose a detailed analysis of several scenarios, including some of the most powerful currently available HPC platforms, as well as anticipated exascale designs.In the third, fourth, and fifth chapters, we study the combination of different fault tolerant mechanisms (replication, fault prediction and detection of silent errors) with the traditional checkpoint/restart mechanism. We evaluated several models using simulations. Our results show that these models are useful for a set of models of applications in the context of future exascale systems.In the second part of the thesis, we study the problem of minimizing the expected sensor data acquisition cost when evaluating a query expressed as a tree of disjunctive Boolean operators applied to Boolean predicates. The problem is to determine the order in which predicates should be evaluated so as to shortcut part of the query evaluation and minimize the expected cost.In the sixth chapter, we present the related work of the second part and in the seventh chapter, we study the problem for queries expressed as a disjunctive normal form. We consider the more general case where each data stream can appear in multiple predicates and we consider two models, the model where each predicate can access a single stream and the model where each predicate can access multiple streams.

The main task of this Masters Thesis is to understand questions of information retrieval and text classifi cation. The main research is focused on the text data, the semantic dictionaries and especially the knowledges inferred from the Wikipedia. In this thesis is also described implementation of the querying system, which is based on achieved knowledges. Finally properties and possible improvements of the system are talked over.

The master's thesis deals with Information Retrieval. It summarizes the knowledge in the field of Information Retrieval theory. Furthermore, the work gives an overview of models used in Information Retrieval, the data and the actual issues and their possible solutions. The practical part of the master's thesis is focused on the implementation of methods of information retrieval in textual data. The last part is dedicated to experiments validating the implementation and its possible improvements.

This thesis focuses on improving the SAT solving technology. The improvements focus on two major subjects: sequential SAT solving and parallel SAT solving. To better understand sequential SAT algorithms, the abstract reduction system Generic CDCL is introduced. With Generic CDCL, the soundness of solving techniques can be modeled. Next, the conflict driven clause learning algorithm is extended with the three techniques local look-ahead, local probing and all UIP learning that allow more global reasoning during search. These techniques improve the performance of the sequential SAT solver Riss. Then, the formula simplification techniques bounded variable addition, covered literal elimination and an advanced cardinality constraint extraction are introduced. By using these techniques, the reasoning of the overall SAT solving tool chain becomes stronger than plain resolution. When using these three techniques in the formula simplification tool Coprocessor before using Riss to solve a formula, the performance can be improved further. Due to the increasing number of cores in CPUs, the scalable parallel SAT solving approach iterative partitioning has been implemented in Pcasso for the multi-core architecture. Related work on parallel SAT solving has been studied to extract main ideas that can improve Pcasso. Besides parallel formula simplification with bounded variable elimination, the major extension is the extended clause sharing level based clause tagging, which builds the basis for conflict driven node killing. The latter allows to better identify unsatisfiable search space partitions. Another improvement is to combine scattering and look-ahead as a superior search space partitioning function. In combination with Coprocessor, the introduced extensions increase the performance of the parallel solver Pcasso. The implemented system turns out to be scalable for the multi-core architecture. Hence iterative partitioning is interesting for future parallel SAT solvers. The implemented solvers participated in international SAT competitions. In 2013 and 2014 Pcasso showed a good performance. Riss in combination with Copro- cessor won several first, second and third prices, including two Kurt-Gödel-Medals. Hence, the introduced algorithms improved modern SAT solving technology.