Avoiding Unnecessary Information Loss: Correct and Efficient Model Synchronization Based on Triple Graph Grammars

Model synchronization, i.e., the task of restoring consistency between two interrelated models after a model change, is a challenging task. Triple Graph Grammars (TGGs) specify model consistency by means of rules that describe how to create consistent pairs of models. These rules can be used to automatically derive further rules, which describe how to propagate changes from one model to the other or how to change one model in such a way that propagation is guaranteed to be possible. Restricting model synchronization to these derived rules, however, may lead to unnecessary deletion and recreation of model elements during change propagation. This is inefficient and may cause unnecessary information loss, i.e., when deleted elements contain information that is not represented in the second model, this information cannot be recovered easily. Short-cut rules have recently been developed to avoid unnecessary information loss by reusing existing model elements. In this paper, we show how to automatically derive (short-cut) repair rules from short-cut rules to propagate changes such that information loss is avoided and model synchronization is accelerated. The key ingredients of our rule-based model synchronization process are these repair rules and an incremental pattern matcher informing about suitable applications of them. We prove the termination and the correctness of this synchronization process and discuss its completeness. As a proof of concept, we have implemented this synchronization process in eMoflon, a state-of-the-art model transformation tool with inherent support of bidirectionality. Our evaluation shows that repair processes based on (short-cut) repair rules have considerably decreased information loss and improved performance compared to former model synchronization processes based on TGGs.Comment: 33 pages, 20 figures, 3 table

NASA space station automation: AI-based technology review. Executive summary

Research and Development projects in automation technology for the Space Station are described. Artificial Intelligence (AI) based technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics

Swiftmend: Data Synchronization in Open mHealth Applications with Restricted Connectivity

Open mHealth applications often include mobile devices and cloud services with replicated data between components. These replicas need periodical synchronization to remain consistent. However, there are no guarantee of connectivity to networks which do not bill users on the quantity of data usage. This thesis propose Swiftmend, a system with synchronization that minimize the quantity of I/O used on the network. Swiftmend includes two reconciliation algorithms; Rejuvenation and Regrowth. The latter utilizes the efficiency of the Merkle tree data structure to reduce the I/O. Merkle trees can sum up the consistency of replicas into compact fingerprints. While the first reconciliation algorithm, Rejuvenation simply inspects the entire replica to identify consistency. Regrowth is shown to produce less quantity of I/O than Rejuvenation when synchronizing replicas. This is due to the compact fingerprints

Formal Foundations for Information-Preserving Model Synchronization Processes Based on Triple Graph Grammars

Zwischen verschiedenen Artefakten, die Informationen teilen, wieder Konsistenz herzustellen, nachdem eines von ihnen geändert wurde, ist ein wichtiges Problem, das in verschiedenen Bereichen der Informatik auftaucht. Mit dieser Dissertation legen wir eine Lösung für das grundlegende Modellsynchronisationsproblem vor. Bei diesem Problem ist ein Paar solcher Artefakte (Modelle) gegeben, von denen eines geändert wurde; Aufgabe ist die Wiederherstellung der Konsistenz. Tripelgraphgrammatiken (TGGs) sind ein etablierter und geeigneter Formalismus, um dieses und verwandte Probleme anzugehen. Da sie auf der algebraischen Theorie der Graphtransformation und dem (Double-)Pushout Zugang zu Ersetzungssystemen basieren, sind sie besonders geeignet, um Lösungen zu entwickeln, deren Eigenschaften formal bewiesen werden können. Doch obwohl TGG-basierte Ansätze etabliert sind, leiden viele von ihnen unter dem Problem des Informationsverlustes. Wenn ein Modell geändert wurde, können während eines Synchronisationsprozesses Informationen verloren gehen, die nur im zweiten Modell vorliegen. Das liegt daran, dass solche Synchronisationsprozesse darauf zurückfallen Konsistenz dadurch wiederherzustellen, dass sie das geänderte Modell (bzw. große Teile von ihm) neu übersetzen. Wir schlagen einen TGG-basierten Ansatz vor, der fortgeschrittene Features von TGGs unterstützt (Attribute und negative Constraints), durchgängig formalisiert ist, implementiert und inkrementell in dem Sinne ist, dass er den Informationsverlust im Vergleich mit vorherigen Ansätzen drastisch reduziert. Bisher gibt es keinen TGG-basierten Ansatz mit vergleichbaren Eigenschaften. Zentraler Beitrag dieser Dissertation ist es, diesen Ansatz formal auszuarbeiten und seine wesentlichen Eigenschaften, nämlich Korrektheit, Vollständigkeit und Termination, zu beweisen. Die entscheidende neue Idee unseres Ansatzes ist es, Reparaturregeln anzuwenden. Dies sind spezielle Regeln, die es erlauben, Änderungen an einem Modell direkt zu propagieren anstatt auf Neuübersetzung zurückzugreifen. Um diese Reparaturregeln erstellen und anwenden zu können, entwickeln wir grundlegende Beiträge zur Theorie der algebraischen Graphtransformation. Zunächst entwickeln wir eine neue Art der sequentiellen Komposition von Regeln. Im Gegensatz zur gewöhnlichen Komposition, die zu Regeln führt, die Elemente löschen und dann wieder neu erzeugen, können wir Regeln herleiten, die solche Elemente stattdessen bewahren. Technisch gesehen findet der Synchronisationsprozess, den wir entwickeln, außerdem in der Kategorie der partiellen Tripelgraphen statt und nicht in der der normalen Tripelgraphen. Daher müssen wir sicherstellen, dass die für Double-Pushout-Ersetzungssysteme ausgearbeitete Theorie immer noch gültig ist. Dazu entwickeln wir eine (kategorientheoretische) Konstruktion neuer Kategorien aus gegebenen und zeigen, dass (i) diese Konstruktion die Axiome erhält, die nötig sind, um die Theorie für Double-Pushout-Ersetzungssysteme zu entwickeln, und (ii) partielle Tripelgraphen als eine solche Kategorie konstruiert werden können. Zusammen ermöglichen diese beiden grundsätzlichen Beiträge es uns, unsere Lösung für das grundlegende Modellsynchronisationsproblem vollständig formal auszuarbeiten und ihre zentralen Eigenschaften zu beweisen.Restoring consistency between different information-sharing artifacts after one of them has been changed is an important problem that arises in several areas of computer science. In this thesis, we provide a solution to the basic model synchronization problem. There, a pair of such artifacts (models), one of which has been changed, is given and consistency shall be restored. Triple graph grammars (TGGs) are an established and suitable formalism to address this and related problems. Being based on the algebraic theory of graph transformation and (double-)pushout rewriting, they are especially suited to develop solutions whose properties can be formally proven. Despite being established, many TGG-based solutions do not satisfactorily deal with the problem of information loss. When one model is changed, in the process of restoring consistency such solutions may lose information that is only present in the second model because the synchronization process resorts to restoring consistency by re-translating (large parts of) the updated model. We introduce a TGG-based approach that supports advanced features of TGGs (attributes and negative constraints), is comprehensively formalized, implemented, and is incremental in the sense that it drastically reduces the amount of information loss compared to former approaches. Up to now, a TGG-based approach with these characteristics is not available. The central contribution of this thesis is to formally develop that approach and to prove its essential properties, namely correctness, completeness, and termination. The crucial new idea in our approach is the use of repair rules, which are special rules that allow one to directly propagate changes from one model to the other instead of resorting to re-translation. To be able to construct and apply these repair rules, we contribute more fundamentally to the theory of algebraic graph transformation. First, we develop a new kind of sequential rule composition. Whereas the conventional composition of rules leads to rules that delete and re-create elements, we can compute rules that preserve such elements instead. Furthermore, technically the setting in which the synchronization process we develop takes place is the category of partial triple graphs and not the one of ordinary triple graphs. Hence, we have to ensure that the elaborate theory of double-pushout rewriting still applies. Therefore, we develop a (category-theoretic) construction of new categories from given ones and show that (i) this construction preserves the axioms that are necessary to develop the theory of double-pushout rewriting and (ii) partial triple graphs can be constructed as such a category. Together, those two more fundamental contributions enable us to develop our solution to the basic model synchronization problem in a fully formal manner and to prove its central properties

PRESENCE: A human-inspired architecture for speech-based human-machine interaction

Recent years have seen steady improvements in the quality and performance of speech-based human-machine interaction driven by a significant convergence in the methods and techniques employed. However, the quantity of training data required to improve state-of-the-art systems seems to be growing exponentially and performance appears to be asymptotic to a level that may be inadequate for many real-world applications. This suggests that there may be a fundamental flaw in the underlying architecture of contemporary systems, as well as a failure to capitalize on the combinatorial properties of human spoken language. This paper addresses these issues and presents a novel architecture for speech-based human-machine interaction inspired by recent findings in the neurobiology of living systems. Called PRESENCE-"PREdictive SENsorimotor Control and Emulation" - this new architecture blurs the distinction between the core components of a traditional spoken language dialogue system and instead focuses on a recursive hierarchical feedback control structure. Cooperative and communicative behavior emerges as a by-product of an architecture that is founded on a model of interaction in which the system has in mind the needs and intentions of a user and a user has in mind the needs and intentions of the system

Parameterized verification and repair of concurrent systems

In this thesis, we present novel approaches for model checking, repair and synthesis of systems that may be parameterized in their number of components. The parameterized model checking problem (PMCP) is in general undecidable, and therefore the focus is on restricted classes of parameterized concurrent systems where the problem is decidable. Under certain conditions, the problem is decidable for guarded protocols, and for systems that communicate via a token, a pairwise, or a broadcast synchronization. In this thesis we improve existing results for guarded protocols and we show that the PMCP of guarded protocols and token passing systems is decidable for specifications that add a quantitative aspect to LTL, called Prompt-LTL. Furthermore, we present, to our knowledge, the first parameterized repair algorithm. The parameterized repair problem is to find a refinement of a process implementation p such that the concurrent system with an arbitrary number of instances of p is correct. We show how this algorithm can be used on classes of systems that can be represented as well structured transition systems (WSTS). Additionally we present two safety synthesis algorithms that utilize a lazy approach. Given a faulty system, the algorithms first symbolically model check the system, then the obtained error traces are analyzed to synthesize a candidate that has no such traces. Experimental results show that our algorithm solves a number of benchmarks that are intractable for existing tools. Furthermore, we introduce our tool AIGEN for generating random Boolean functions and transition systems in a symbolic representation.In dieser Arbeit stellen wir neuartige Ans atze für das Model-Checking, die Reparatur und die Synthese von Systemen vor, die in ihrer Anzahl von Komponenten parametrisiert sein können. Das Problem des parametrisierten Model-Checking (PMCP) ist im Allgemeinen unentscheidbar, und daher liegt der Fokus auf eingeschränkten Klassen parametrisierter synchroner Systeme, bei denen das Problem entscheidbar ist. Unter bestimmten Bedingungen ist das Problem für Guarded Protocols und für Systeme, die über ein Token, eine Pairwise oder eine Broadcast-Synchronisation kommunizieren, entscheidbar. In dieser Arbeit verbessern wir bestehende Ergebnisse für Guarded Protocols und zeigen die Entscheidbarkeit des PMCP für Guarded Protocols und Token-Passing Systeme mit Spezifikationen in der temporalen Logik Prompt-LTL, die LTL einen quantitativen Aspekt hinzufügt. Darüber hinaus präsentieren wir unseres Wissens den ersten parametrisierten Reparaturalgorithmus. Das parametrisierte Reparaturproblem besteht darin, eine Verfeinerung einer Prozessimplementierung p zu finden, so dass das synchrone Systeme mit einer beliebigen Anzahl von Instanzen von p korrekt ist. Wir zeigen, wie dieser Algorithmus auf Klassen von Systemen angewendet werden kann, die als Well Structured Transition Systems (WSTS) dargestellt werden können. Außerdem präsentieren wir zwei Safety-Synthesis Algorithmen, die einen "lazy" Ansatz verwenden. Bei einem fehlerhaften System überprüfen die Algorithmen das System symbolisch, dann werden die erhaltenen "Gegenbeispiel" analysiert, um einen Kandidaten zu synthetisieren der keine solchen Fehlerpfade hat. Versuchsergebnisse zeigen, dass unser Algorithmus eine Reihe von Benchmarks löst, die für bestehende Tools nicht lösbar sind. Darüber hinaus stellen wir unser Tool AIGEN zur Erzeugung zufälliger Boolescher Funktionen und Transitionssysteme in einer symbolischen Darstellung vor