Un Modèle basé sur les Grammaires Attribuées Gardées pour les Processus Dynamiques, Centrés sur l’Utilisateur, Distribués et Collaboratifs: Cas de la Surveillance Epidémiologique

Dynamic processes in which users need to work together and collaborate in myriad ways on process models defined on-the-fly are fast becoming the rule rather than the exception. This thesis presents the design of a purely declarative modelling approach for dynamic, collaborative, user-centred, and data-driven processes. First, we organize the work of a user into task hierarchies which we model as mindmaps, which are trees used to visualize, organize, and log information about tasks in which the user is involved. We introduce the model of guarded attribute grammars, or GAG, to help the automation of updating such maps. A GAG consists of an underlying grammar, that specifies the logical structure of the map, with semantic rules which are used both to govern the evolution of the tree structure (how an open node may be rened to a sub-tree) and to compute the values of some of its attributes. The map enriched with this extra information and with high-level constructs for task dependencies; collaborationand user-interactions is termed an active workspace or AW. Communication between AWs is essentially through the exchange of messages without a shared memory thus enabling convenient distribution on an asynchronous architecture. Lastly, we introduce a language syntax for GAG specification and design a prototype that includes an internal domain specific language (in Haskell) for their specification and a graphical user interface to simulate its execution in a distributed environment. We motivate our approach and illustrate its language syntax and features on a case study for a disease surveillance system.De plus en plus, les utilisateurs collaborent de multiples façons sur des processus dynamiques construit de manière progressive. Dans cette thèse, nous concevons une nouvelle approche d´déclarative de modélisation des processus dynamiques, centrés sur l’utilisateur et dirigés par les données. Tout d’abord, nous organisons le travail d’un utilisateur par des hiérarchies des taches, représentées par des cartes heuristiques (arbre de taches). Ces derniers sont utilisés pour visualiser, organiser, et sauvegarder les informations sur les tâches menés par l’utilisateur. Nous introduisons ensuite le modèle des grammaires attribuées gardées, ou GAG, pour faciliter l’automatisation de la manipulation de telles cartes. Une GAG consiste en une grammaire sous-jacente, qui spécifie la structure logique de la carte, avec des règles sémantiques qui servent à la fois a` gouverner l’évolution de l’arbre des taches (raffinement des nœuds ouverts) et a` calculer les valeurs de certains de ses attributs. La carte enrichie de ces informations supplémentaires et d’autres concepts de haut niveau pour les d´dépendances entre les taches, la collaboration et les interactions utilisateur est appelée Active Workspace ou AW. La communication entre AWs est essentiellement par échange des messages permettant ainsi une implémentation commode sur une architecture distribuée et asynchrone. Enfin, nous décrivons une syntaxe de langage pour la spécification des processus en utilisant les GAGs et concevons un prototype qui inclut un langage spécifique au domaine, interne `a Haskell, pour leur spécification et une interface utilisateur graphique pour la simulation de l’exécution dans un environnement distribué. Nous motivons notre approche et illustrons sa syntaxe et ses caractéristiques sur une étude de cas portant sur le processus de surveillance épidémiologique

Evaluating the performance of model transformation styles in Maude

Rule-based programming has been shown to be very successful in many application areas. Two prominent examples are the specification of model transformations in model driven development approaches and the definition of structured operational semantics of formal languages. General rewriting frameworks such as Maude are flexible enough to allow the programmer to adopt and mix various rule styles. The choice between styles can be biased by the programmer’s background. For instance, experts in visual formalisms might prefer graph-rewriting styles, while experts in semantics might prefer structurally inductive rules. This paper evaluates the performance of different rule styles on a significant benchmark taken from the literature on model transformation. Depending on the actual transformation being carried out, our results show that different rule styles can offer drastically different performances. We point out the situations from which each rule style benefits to offer a valuable set of hints for choosing one style over the other

Semantically defined Analytics for Industrial Equipment Diagnostics

In this age of digitalization, industries everywhere accumulate massive amount of data such that it has become the lifeblood of the global economy. This data may come from various heterogeneous systems, equipment, components, sensors, systems and applications in many varieties (diversity of sources), velocities (high rate of changes) and volumes (sheer data size). Despite significant advances in the ability to collect, store, manage and filter data, the real value lies in the analytics. Raw data is meaningless, unless it is properly processed to actionable (business) insights. Those that know how to harness data effectively, have a decisive competitive advantage, through raising performance by making faster and smart decisions, improving short and long-term strategic planning, offering more user-centric products and services and fostering innovation. Two distinct paradigms in practice can be discerned within the field of analytics: semantic-driven (deductive) and data-driven (inductive). The first emphasizes logic as a way of representing the domain knowledge encoded in rules or ontologies and are often carefully curated and maintained. However, these models are often highly complex, and require intensive knowledge processing capabilities. Data-driven analytics employ machine learning (ML) to directly learn a model from the data with minimal human intervention. However, these models are tuned to trained data and context, making it difficult to adapt. Industries today that want to create value from data must master these paradigms in combination. However, there is great need in data analytics to seamlessly combine semantic-driven and data-driven processing techniques in an efficient and scalable architecture that allows extracting actionable insights from an extreme variety of data. In this thesis, we address these needs by providing: • A unified representation of domain-specific and analytical semantics, in form of ontology models called TechOnto Ontology Stack. It is highly expressive, platform-independent formalism to capture conceptual semantics of industrial systems such as technical system hierarchies, component partonomies etc and its analytical functional semantics. • A new ontology language Semantically defined Analytical Language (SAL) on top of the ontology model that extends existing DatalogMTL (a Horn fragment of Metric Temporal Logic) with analytical functions as first class citizens. • A method to generate semantic workflows using our SAL language. It helps in authoring, reusing and maintaining complex analytical tasks and workflows in an abstract fashion. • A multi-layer architecture that fuses knowledge- and data-driven analytics into a federated and distributed solution. To our knowledge, the work in this thesis is one of the first works to introduce and investigate the use of the semantically defined analytics in an ontology-based data access setting for industrial analytical applications. The reason behind focusing our work and evaluation on industrial data is due to (i) the adoption of semantic technology by the industries in general, and (ii) the common need in literature and in practice to allow domain expertise to drive the data analytics on semantically interoperable sources, while still harnessing the power of analytics to enable real-time data insights. Given the evaluation results of three use-case studies, our approach surpass state-of-the-art approaches for most application scenarios.Im Zeitalter der Digitalisierung sammeln die Industrien überall massive Daten-mengen, die zum Lebenselixier der Weltwirtschaft geworden sind. Diese Daten können aus verschiedenen heterogenen Systemen, Geräten, Komponenten, Sensoren, Systemen und Anwendungen in vielen Varianten (Vielfalt der Quellen), Geschwindigkeiten (hohe Änderungsrate) und Volumina (reine Datengröße) stammen. Trotz erheblicher Fortschritte in der Fähigkeit, Daten zu sammeln, zu speichern, zu verwalten und zu filtern, liegt der eigentliche Wert in der Analytik. Rohdaten sind bedeutungslos, es sei denn, sie werden ordnungsgemäß zu verwertbaren (Geschäfts-)Erkenntnissen verarbeitet. Wer weiß, wie man Daten effektiv nutzt, hat einen entscheidenden Wettbewerbsvorteil, indem er die Leistung steigert, indem er schnellere und intelligentere Entscheidungen trifft, die kurz- und langfristige strategische Planung verbessert, mehr benutzerorientierte Produkte und Dienstleistungen anbietet und Innovationen fördert. In der Praxis lassen sich im Bereich der Analytik zwei unterschiedliche Paradigmen unterscheiden: semantisch (deduktiv) und Daten getrieben (induktiv). Die erste betont die Logik als eine Möglichkeit, das in Regeln oder Ontologien kodierte Domänen-wissen darzustellen, und wird oft sorgfältig kuratiert und gepflegt. Diese Modelle sind jedoch oft sehr komplex und erfordern eine intensive Wissensverarbeitung. Datengesteuerte Analysen verwenden maschinelles Lernen (ML), um mit minimalem menschlichen Eingriff direkt ein Modell aus den Daten zu lernen. Diese Modelle sind jedoch auf trainierte Daten und Kontext abgestimmt, was die Anpassung erschwert. Branchen, die heute Wert aus Daten schaffen wollen, müssen diese Paradigmen in Kombination meistern. Es besteht jedoch ein großer Bedarf in der Daten-analytik, semantisch und datengesteuerte Verarbeitungstechniken nahtlos in einer effizienten und skalierbaren Architektur zu kombinieren, die es ermöglicht, aus einer extremen Datenvielfalt verwertbare Erkenntnisse zu gewinnen. In dieser Arbeit, die wir auf diese Bedürfnisse durch die Bereitstellung: • Eine einheitliche Darstellung der Domänen-spezifischen und analytischen Semantik in Form von Ontologie Modellen, genannt TechOnto Ontology Stack. Es ist ein hoch-expressiver, plattformunabhängiger Formalismus, die konzeptionelle Semantik industrieller Systeme wie technischer Systemhierarchien, Komponenten-partonomien usw. und deren analytische funktionale Semantik zu erfassen. • Eine neue Ontologie-Sprache Semantically defined Analytical Language (SAL) auf Basis des Ontologie-Modells das bestehende DatalogMTL (ein Horn fragment der metrischen temporären Logik) um analytische Funktionen als erstklassige Bürger erweitert. • Eine Methode zur Erzeugung semantischer workflows mit unserer SAL-Sprache. Es hilft bei der Erstellung, Wiederverwendung und Wartung komplexer analytischer Aufgaben und workflows auf abstrakte Weise. • Eine mehrschichtige Architektur, die Wissens- und datengesteuerte Analysen zu einer föderierten und verteilten Lösung verschmilzt. Nach unserem Wissen, die Arbeit in dieser Arbeit ist eines der ersten Werke zur Einführung und Untersuchung der Verwendung der semantisch definierten Analytik in einer Ontologie-basierten Datenzugriff Einstellung für industrielle analytische Anwendungen. Der Grund für die Fokussierung unserer Arbeit und Evaluierung auf industrielle Daten ist auf (i) die Übernahme semantischer Technologien durch die Industrie im Allgemeinen und (ii) den gemeinsamen Bedarf in der Literatur und in der Praxis zurückzuführen, der es der Fachkompetenz ermöglicht, die Datenanalyse auf semantisch inter-operablen Quellen voranzutreiben, und nutzen gleichzeitig die Leistungsfähigkeit der Analytik, um Echtzeit-Daten-einblicke zu ermöglichen. Aufgrund der Evaluierungsergebnisse von drei Anwendungsfällen Übertritt unser Ansatz für die meisten Anwendungsszenarien Modernste Ansätze

Pseudo-contractions as Gentle Repairs

Updating a knowledge base to remove an unwanted consequence is a challenging task. Some of the original sentences must be either deleted or weakened in such a way that the sentence to be removed is no longer entailed by the resulting set. On the other hand, it is desirable that the existing knowledge be preserved as much as possible, minimising the loss of information. Several approaches to this problem can be found in the literature. In particular, when the knowledge is represented by an ontology, two different families of frameworks have been developed in the literature in the past decades with numerous ideas in common but with little interaction between the communities: applications of AGM-like Belief Change and justification-based Ontology Repair. In this paper, we investigate the relationship between pseudo-contraction operations and gentle repairs. Both aim to avoid the complete deletion of sentences when replacing them with weaker versions is enough to prevent the entailment of the unwanted formula. We show the correspondence between concepts on both sides and investigate under which conditions they are equivalent. Furthermore, we propose a unified notation for the two approaches, which might contribute to the integration of the two areas

Knowledge-base and techniques for effective service-oriented programming & management of hybrid processes

Recent advances in Web 2.0, SOA, crowd-sourcing, social and collaboration technologies, as well as cloud-computing, have truly transformed the Internet into a global development and deployment platform. As a result, developers have been presented with ubiquitous access to countless Web-services, resources and tools. However, while enabling tremendous automation and reuse opportunities, new productivity challenges have also emerged: The exploitation of services and resources nonetheless requires skilled programmers and a development-centric approach; it is thus inevitably susceptible to the same repetitive, error-prone and time consuming integration work each time a developer integrates a new API. Business Process Management on the other hand were proposed to support service-based integration. It provided the benefit of automation and modelling, which appealed to non-technical domain-experts. The problem however: it proves too rigid for unstructured processes. Thus, without this level of support, building new application either requires extensive manual programming or resorting to homebrew solutions. Alternatively, with the proliferation of SaaS, various such tools could be used for independent portions of the overall process - although this either presupposes conforming to the in-built process, or results in "shadow processes" via use of e-mail or the like, in order to exchange information and share decisions. There has therefore been an inevitable gap in technological support between structured and unstructured processes. To address these challenges, this thesis deals with transitioning process-support from structured to unstructured. We have been motivated to harness the foundational capabilities of BPM for its application to unstructured processes. We propose to achieve this by: First, addressing the productivity challenges of Web-services integration - simplifying this process - whilst encouraging an incremental curation and collective reuse approach. We then extend this to propose an innovative Hybrid-Process Management Platform that holistically combines structured, semi-structured and unstructured activities, based on a unified task-model that encapsulates a spectrum of process specificity. We have thus aimed to bridge the current lacking technology gap. The approach presented has been exposed as service-based libraries and tools. Whereby, we have devised several use-case scenarios and conducted user-studies in order to evaluate the overall effectiveness of our proposed work

Trustworthy Formal Natural Language Specifications

Interactive proof assistants are computer programs carefully constructed to check a human-designed proof of a mathematical claim with high confidence in the implementation. However, this only validates truth of a formal claim, which may have been mistranslated from a claim made in natural language. This is especially problematic when using proof assistants to formally verify the correctness of software with respect to a natural language specification. The translation from informal to formal remains a challenging, time-consuming process that is difficult to audit for correctness. This paper shows that it is possible to build support for specifications written in expressive subsets of natural language, within existing proof assistants, consistent with the principles used to establish trust and auditability in proof assistants themselves. We implement a means to provide specifications in a modularly extensible formal subset of English, and have them automatically translated into formal claims, entirely within the Lean proof assistant. Our approach is extensible (placing no permanent restrictions on grammatical structure), modular (allowing information about new words to be distributed alongside libraries), and produces proof certificates explaining how each word was interpreted and how the sentence's structure was used to compute the meaning. We apply our prototype to the translation of various English descriptions of formal specifications from a popular textbook into Lean formalizations; all can be translated correctly with a modest lexicon with only minor modifications related to lexicon size.Comment: arXiv admin note: substantial text overlap with arXiv:2205.0781

HPM-Frame: A Decision Framework for Executing Software on Heterogeneous Platforms

Heterogeneous computing is one of the most important computational solutions to meet rapidly increasing demands on system performance. It typically allows the main flow of applications to be executed on a CPU while the most computationally intensive tasks are assigned to one or more accelerators, such as GPUs and FPGAs. The refactoring of systems for execution on such platforms is highly desired but also difficult to perform, mainly due the inherent increase in software complexity. After exploration, we have identified a current need for a systematic approach that supports engineers in the refactoring process -- from CPU-centric applications to software that is executed on heterogeneous platforms. In this paper, we introduce a decision framework that assists engineers in the task of refactoring software to incorporate heterogeneous platforms. It covers the software engineering lifecycle through five steps, consisting of questions to be answered in order to successfully address aspects that are relevant for the refactoring procedure. We evaluate the feasibility of the framework in two ways. First, we capture the practitioner's impressions, concerns and suggestions through a questionnaire. Then, we conduct a case study showing the step-by-step application of the framework using a computer vision application in the automotive domain.Comment: Manuscript submitted to the Journal of Systems and Softwar

Survey of Template-Based Code Generation

L'automatisation de la génération des artefacts textuels à partir des modèles est une étape critique dans l'Ingénierie Dirigée par les Modèles (IDM). C'est une transformation de modèles utile pour générer le code source, sérialiser les modèles dans de stockages persistents, générer les rapports ou encore la documentation. Parmi les différents paradigmes de transformation de modèle-au-texte, la génération de code basée sur les templates (TBCG) est la plus utilisée en IDM. La TBCG est une technique de génération qui produit du code à partir des spécifications de haut niveau appelées templates. Compte tenu de la diversité des outils et des approches, il est nécessaire de classifier et de comparer les techniques de TBCG existantes afin d'apporter un soutien approprié aux développeurs. L'objectif de ce mémoire est de mieux comprendre les caractéristiques des techniques de TBCG, identifier les tendances dans la recherche, et éxaminer l'importance du rôle de l'IDM par rapport à cette approche. J'évalue également l'expressivité, la performance et la mise à l'échelle des outils associés selon une série de modèles. Je propose une étude systématique de cartographie de la littérature qui décrit une intéressante vue d'ensemble de la TBCG et une étude comparitive des outils de la TBCG pour mieux guider les dévloppeurs dans leur choix. Cette étude montre que les outils basés sur les modèles offrent plus d'expressivité tandis que les outils basés sur le code sont les plus performants. Enfin, Xtend2 offre le meilleur compromis entre l'expressivité et la performance.A critical step in model-driven engineering (MDE) is the automatic synthesis of a textual artifact from models. This is a very useful model transformation to generate application code, to serialize the model in persistent storage, generate documentation or reports. Among the various model-to-text transformation paradigms, Template-Based Code Generation (TBCG) is the most popular in MDE. TBCG is a synthesis technique that produces code from high-level specifications, called templates. It is a popular technique in MDE given that they both emphasize abstraction and automation. Given the diversity of tools and approaches, it is necessary to classify and compare existing TBCG techniques to provide appropriate support to developers. The goal of this thesis is to better understand the characteristics of TBCG techniques, identify research trends, and assess the importance of the role of MDE in this code synthesis approach. We also evaluate the expressiveness, performance and scalability of the associated tools based on a range of models that implement critical patterns. To this end, we conduct a systematic mapping study of the literature that paints an interesting overview of TBCG and a comparative study on TBCG tools to better guide developers in their choices. This study shows that model-based tools offer more expressiveness whereas code-based tools performed much faster. Xtend2 offers the best compromise between the expressiveness and the performance