Les systèmes dynamiques présentent des comportements temporels qui peuvent être exprimés sous diverses formes séquentielles telles que des signaux, des ondes, des séries chronologiques et des suites d'événements. Détecter des motifs sur de tels comportements temporels est une tâche fondamentale pour comprendre et évaluer ces systèmes. Étant donné que de nombreux comportements du système impliquent certaines caractéristiques temporelles, le besoin de spécifier et de détecter des motifs de comportements qui implique des exigences de synchronisation, appelées motifs temporisés, est évidente.Cependant, il s'agit d'une tâche non triviale due à un certain nombre de raisons, notamment la concomitance des sous-systèmes et la densité de temps.La contribution principale de cette thèse est l'introduction et le développement du filtrage par motif temporisé, c'est-à-dire l'identification des segments d'un comportement donné qui satisfont un motif temporisé. Nous proposons des expressions rationnelles temporisées (TRE) et la logique de la boussole métrique (MCL) comme langages de spécification pour motifs temporisés. Nous développons d'abord un nouveau cadre qui abstraite le calcul des aspects liés au temps appelé l'algèbre des relations temporisées. Ensuite, nous fournissons des algorithmes du filtrage hors ligne pour TRE et MCL sur des comportements à temps dense à valeurs discrètes en utilisant ce cadre et étudions quelques extensions pratiques.Il est nécessaire pour certains domaines d'application tels que le contrôle réactif que le filtrage par motif doit être effectué pendant l'exécution réelle du système. Pour cela, nous fournissons un algorithme du filtrage en ligne pour TREs basé sur la technique classique des dérivées d'expressions rationnelles. Nous croyons que la technique sous-jacente qui combine les dérivées et les relations temporisées constitue une autre contribution conceptuelle majeure pour la recherche sur les systèmes temporisés.Nous présentons un logiciel libre Montre qui implémente nos idées et algorithmes. Nous explorons diverses applications du filtrage par motif temporisé par l'intermédiaire de plusieurs études de cas. Enfin, nous discutons des orientations futures et plusieurs questions ouvertes qui ont émergé à la suite de cette thèse. / Dynamical systems exhibit temporal behaviors that can be expressed in various sequential forms such as signals, waveforms, time series, and event sequences. Detecting patterns over such temporal behaviors is a fundamental task for understanding and assessing these systems. Since many system behaviors involve certain timing characteristics, the need to specify and detect patterns of behaviors that involves timing requirements, called timed patterns, is evident. However, this is a non-trivial task due to a number of reasons including the concurrency of subsystems and density of time.The key contribution of this thesis is in introducing and developing emph{timed pattern matching}, that is, the act of identifying segments of a given behavior that satisfy a timed pattern. We propose timed regular expressions (TREs) and metric compass logic (MCL) as timed pattern specification languages. We first develop a novel framework that abstracts the computation of time-related aspects called the algebra of timed relations. Then we provide offline matching algorithms for TRE and MCL over discrete-valued dense-time behaviors using this framework and study some practical extensions.It is necessary for some application areas such as reactive control that pattern matching needs to be performed during the actual execution of the system. For that, we provide an online matching algorithm for TREs based on the classical technique of derivatives of regular expressions. We believe the underlying technique that combines derivatives and timed relations constitutes another major conceptual contribution for timed systems research.Furthermore, we present an open-source tool Montre that implements our ideas and algorithms. We explore diverse applications of timed pattern matching over several case studies using Montre. Finally we discuss future directions and several open questions emerged as a result of this thesis.

Spam se typicky nevyskytuje ve formě samostatných zpráv, ale často bývá sdružován do takzvaných kampaní. Ty bývají automaticky generovány pomocí šablon. Díky tomu jsou jednotlivé zprávy sémanticky, ale ne syntakticky, ekvivalentní. Cílem práce je navrhnout algoritmus schopný z množiny zpráv jedné kampaně zpětně extrahovat šablonu, ze které tyto zprávy byly generovány. Práce se zaměřuje na spam v SMS komunikaci, ale navržené postupy jsou dostatečně obecné pro širší použití. Algoritmus je postaven na metodě zarovnávání dvou sekvencí, používané v bioinformatice pro nalezení podobných oblastí proteinových řetězců. Výstupem je regulární výraz popisující šablonu dané kampaně. Součástí řešení je také nástroj pro vizualizaci šablony pomocí HTML.Řešení bylo ověřeno na přibližně třech stovkách skutečných kampaní z celého světa. V naprosté většině případů je poskytnutý výsledek postačující pro identifikaci kampaně.

Cette thèse a pour but d’étudier des propriétés structurelles d’automates étendant celle du déterminisme, et les langages pouvant être dénotés par une expression rationnelle dont l’automate des positions présente l’une de ces propriétés. Si Book et al. ont montré que tous les langages rationnels peuvent être reconnus par un automate des positions non-ambigu, Brüggemann-Klein et Wood ont montré que ceux pouvant l’être par un automate des positions déterministe forment une famille strictement incluse dans celle des rationnels. Nous nous intéressons aux extensions de cette famille, en cherchant à caractériser leurs langages, et à étudier leur hiérarchie interne et leur inclusion entre elles. / This thesis aims to study structural properties of automata extending determinism, and the languages that can be denoted by a regular expression of which the position automaton has one such property. If Book et al. showed that all regular languages can be recognized by an unambiguous position automaton, Brüggemann-Klein and Wood showed that only a proper subset of them can be recognized by a deterministic position automaton. We focus on extensions of this subfamily, by seeking to characterize their languages, and to study their internal hierarchy and how they relate to each other.

Although XML is a widely used technology, the majority of real-world XML documents does not conform to any particular schema. To fill the gap, the research area of automatic schema inference from XML documents has emerged. This work refines and extends recent approaches to the automatic schema inference mainly by exploiting an obsolete schema in the inference process, designing new MDL measures and heuristic excluding of excentric data inputs. The work delivers a ready-to-use and easy-to-extend implementation integrated into the jInfer framework (developed as a software project). Experimental results are a part of the work.

<p>In summary there currently exist techniques to discover miRNA however both require many calculations to be performed during the identification limiting their use at a genomic level. Machine learning techniques are currently providing the best results by combining a number of calculated and statistically derived features to identify miRNA candidates, however almost all of these still include computationally intensive secondary-structure calculations. It is the aim of this project to produce a miRNA identification process that minimises and simplifies the number of computational elements required during the identification process.</p>

<p>In summary there currently exist techniques to discover miRNA however both require many calculations to be performed during the identification limiting their use at a genomic level. Machine learning techniques are currently providing the best results by combining a number of calculated and statistically derived features to identify miRNA candidates, however almost all of these still include computationally intensive secondary-structure calculations. It is the aim of this project to produce a miRNA identification process that minimises and simplifies the number of computational elements required during the identification process.</p>

Magister Scientiae - MSc / In summary there currently exist techniques to discover miRNA however both require many calculations to be performed during the identification limiting their use at a genomic level. Machine learning techniques are currently providing the best results by combining a number of calculated and statistically derived features to identify miRNA candidates, however almost all of these still include computationally intensive secondary-structure calculations. It is the aim of this project to produce a miRNA identification process that minimises and simplifies the number of computational elements required during the identification process. / South Africa

The classic theorems of Büchi and Kleene state the expressive equivalence of finite automata to monadic second order logic and regular expressions, respectively. These fundamental results enjoy applications in nearly every field of theoretical computer science. Around the same time as Büchi and Kleene, Rabin investigated probabilistic finite automata. This equally well established model has applications ranging from natural language processing to probabilistic model checking. Here, we give probabilistic extensions Büchi\\\''s theorem and Kleene\\\''s theorem to the probabilistic setting. We obtain a probabilistic MSO logic by adding an expected second order quantifier. In the scope of this quantifier, membership is determined by a Bernoulli process. This approach turns out to be universal and is applicable for finite and infinite words as well as for finite trees. In order to prove the expressive equivalence of this probabilistic MSO logic to probabilistic automata, we show a Nivat-theorem, which decomposes a recognisable function into a regular language, homomorphisms, and a probability measure. For regular expressions, we build upon existing work to obtain probabilistic regular expressions on finite and infinite words. We show the expressive equivalence between these expressions and probabilistic Muller-automata. To handle Muller-acceptance conditions, we give a new construction from probabilistic regular expressions to Muller-automata. Concerning finite trees, we define probabilistic regular tree expressions using a new iteration operator, called infinity-iteration. Again, we show that these expressions are expressively equivalent to probabilistic tree automata. On a second track of our research we investigate Constraint LTL over multidimensional data words with data values from the infinite tree. Such LTL formulas are evaluated over infinite words, where every position possesses several data values from the infinite tree. Within Constraint LTL on can compare these values from different positions. We show that the model checking problem for this logic is PSPACE-complete via investigating the emptiness problem of Constraint Büchi automata.

Journal articles are the formal medium for the communication of results among scientists, and often contain valuable data. However, manually collecting article data from a large field like lithium-ion battery chemistry is tedious and time consuming, which is an obstacle when searching for statistical trends and correlations to inform research decisions. To address this a platform for the automatic retrieval and analysis of large numbers of articles is created and applied to the field of lithium-ion battery chemistry. Example data produced by the platform is presented and evaluated and sources of error limiting this type of platform are identified, with problems related to text extraction and pattern matching being especially significant. Some solutions to these problems are presented and potential future improvements are proposed.

Mit der steigenden Nachfrage nach Innovationen spielt Software in verschiedenenWirtschaftsbereichen eine wichtige Rolle, wie z.B. in der Automobilindustrie, bei intelligenten Systemen als auch bei Kommunikationssystemen. Daher ist die Qualität für die Softwareentwicklung von großer Bedeutung. Allerdings ändern sich die probabilistische Modelle (die Qualitätsbewertungsmodelle) angesichts der dynamischen Natur moderner Softwaresysteme. Dies führt dazu, dass ihre Übergangswahrscheinlichkeiten im Laufe der Zeit schwanken, welches zu erheblichen Problemen führt. Dahingehend werden probabilistische Modelle im Hinblick auf ihre Laufzeit kontinuierlich aktualisiert. Eine fortdauernde Neubewertung komplexer Wahrscheinlichkeitsmodelle ist jedoch teuer. In letzter Zeit haben sich inkrementelle Ansätze als vielversprechend für die Verifikation von adaptiven Systemen erwiesen. Trotzdem wurden bei der Bewertung struktureller Änderungen im Modell noch keine wesentlichen Verbesserungen erzielt. Wahrscheinlichkeitssysteme werden als Automaten modelliert, wie bei Markov-Modellen. Solche Modelle können in Matrixform dargestellt werden, um die Gleichungen basierend auf Zuständen und Übergangswahrscheinlichkeiten zu lösen. Laufzeitmodelle wie Matrizen sind nicht signifikant, um die Auswirkungen von Modellveränderungen erkennen zu können. In dieser Arbeit wird ein Framework unter Verwendung stochastischer Bäume mit regulären Ausdrücken entwickelt, welches modular aufgebaut ist und eine aktionshaltige sowie probabilistische Logik im Kontext der Modellprüfung aufweist. Ein solches modulares Framework ermöglicht dem Menschen die Entwicklung der Änderungsoperationen für die inkrementelle Berechnung lokaler Änderungen, die im Modell auftreten können. Darüber hinaus werden probabilistische Änderungsmuster beschrieben, um eine effiziente inkrementelle Verifizierung, unter Verwendung von Bäumen mit regulären Ausdrücken, anwenden zu können. Durch die Bewertung der Ergebnisse wird der Vorgang abgeschlossen. / Software plays an innovative role in many different domains, such as car industry, autonomous and smart systems, and communication. Hence, the quality of the software is of utmost importance and needs to be properly addressed during software evolution. Several approaches have been developed to evaluate systems’ quality attributes, such as reliability, safety, and performance of software. Due to the dynamic nature of modern software systems, probabilistic models representing the quality of the software and their transition probabilities change over time and fluctuate, leading to a significant problem that needs to be solved to obtain correct evaluation results of quantitative properties. Probabilistic models need to be continually updated at run-time to solve this issue. However, continuous re-evaluation of complex probabilistic models is expensive. Recently, incremental approaches have been found to be promising for the verification of evolving and self-adaptive systems. Nevertheless, substantial improvements have not yet been achieved for evaluating structural changes in the model. Probabilistic systems are usually represented in a matrix form to solve the equations based on states and transition probabilities. On the other side, evolutionary changes can create various effects on theese models and force them to re-verify the whole system. Run-time models, such as matrices or graph representations, lack the expressiveness to identify the change effect on the model. In this thesis, we develop a framework using stochastic regular expression trees, which are modular, with action-based probabilistic logic in the model checking context. Such a modular framework enables us to develop change operations for the incremental computation of local changes that can occur in the model. Furthermore, we describe probabilistic change patterns to apply efficient incremental quantitative verification using stochastic regular expression trees and evaluate our results.