Type-driven Synthesis of Evolving Data Mode

Modern commercial software is often framed under the umbrella of data-centric applications. Data-centric applications define data as the main and permanent asset. These applications use a single data model for application functionality, data management, and analytical activities, which is built before the applications. Moreover, since applications are temporary, in contrast to data, there is the need to continuously evolve and change the data schema to accommodate new functionality. In this sense, the continuously evolving (rich) feature set that is expected of state-of-the-art applications is intrinsically bound by not only the amount of available data but also by its structure, its internal dependencies, and by the ability to transparently and uniformly grow and evolve data representations and their properties on the fly. The GOLEM project aims to produce new methods of program automation integrated in the development of data-centric applications in low-code frameworks. In this context, one of the key targets for automation is the data layer itself, encompassing the data layout and its integrity constraints, as well as validation and access control rules. The aim of this dissertation, which is integrated in GOLEM, is to develop a synthesis framework that, based on high-level specifications, correctly defines and evolves a rich data layer component by means of high-level operations. The construction of the framework was approached by defining a specification language to express richly-typed specifications, a target language which is the goal of synthesis and a type-directed synthesis procedure based on proof-search concepts. The range of real database operations the framework is able to synthesize is demonstrated through a case study. In a component-based synthesis style, with an extensible library of base operations on database tables (specified using the target language) in context, the case study shows that the synthesis framework is capable of expressing and solving a wide variety of data schema creation and evolution problems.Os sistemas modernos de software comercial são frequentemente caracterizados como aplicações centradas em dados. Estas aplicações definem os dados como o seu principal e persistente ativo, e utilizam um único modelo de dados para as suas funcionalidades, gestão de dados, e atividades analíticas. Além disso, uma vez que as aplicações são efémeras, contrariamente aos dados, existe a necessidade de continuamente evoluir o esquema de dados para introduzir novas funcionalidades. Neste sentido, o conjunto rico de características e em constante evolução que é esperado das aplicações modernas encontra-se restricto, não só pela quantidade de dados disponíveis, mas também pela sua estrutura, dependências internas, e a capacidade de crescer e evoluir a representação dos dados de uma forma uniforme e rápida. O projeto GOLEM tem como objetivo a produção de novos métodos de automação de programas integrado no desenvolvimento de aplicações centradas nos dados em sistemas low-code. Neste contexto, um dos objetivos principais de automação é a camada de dados, compreendendo a estrutura dos dados e as respectivas condições de integridade, como também as regras de validação e controlo de acessos. O objetivo desta dissertação, integrada no projeto GOLEM, é o desenvolvimento de um sistema de síntese que, baseado em especificações de alto nível, define e evolui corretamente uma camada de dados rica com recurso a operações de alto nível. A construção deste sistema baseia-se na definição de uma linguagem de especificação que permite definir especificações com tipos ricos, uma linguagem de expressões que é considerada o objetivo da síntese e um procedimento de síntese orientada pelos tipos. O espectro de operações reais de bases de dados que o sistema consegue sintetizar é demonstrado através de um caso de estudo. Com uma biblioteca extensível de operações sobre tabelas no contexto, o caso de estudo demonstra que o sistema de síntese é capaz de expressar e resolver uma grande variedade de problemas de criação e evolução de esquemas de dados

The semantic Web : theories, languages, and applications

La popularité croissante du Web permet la diffusion d’une quantité phénoménale d’information de toutes sortes et l’accès à une panoplie de services en ligne en raison du développement effréné de ses contenus et de leur consultation quotidienne à faible coût. Malheureusement, cette explosion du Web cause un problème de surabondance de données pas toujours crédibles et souvent inutilisables; les réponses obtenues des engins de recherche peuvent être inadéquates ou imprécises et les services en ligne sont exclusifs ou incompatibles entre eux. Dans le but de pallier à ces inconvénients, le consortium W3C a proposé une solution globale, connue sous le nom de Web sémantique, qui améliore les structures de représentation des données de façon à rendre les contenus signifiants et à permettre l’inférence de nouvelles connaissances par des programmes. Ce mémoire explore les théories sous-jacentes au Web sémantique ainsi que les technologies qui lui sont propres. D’une part, les concepts de logique descriptive et de structure ontologique sont présentés et des liens sont établis entre eux. D’autre part, une hiérarchie de langages incluant, entre autres, les langages XML, RDF, DAML+OIL et OWL est introduite ainsi qu’une étude comparative de plusieurs moteurs d’inférence basés sur ces langages. Enfin, ce mémoire présente un exemple complet qui permet d’illustrer les principaux concepts du Web sémantique et d’évaluer la faisabilité de la mise en oeuvre d’une application par rapport à l’état actuel des technologies

Design of a goal ontology for medical decision-support

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2005.Includes bibliographical references (leaves 34-36).Objectives: There are several ongoing efforts aimed at developing formal models of medical knowledge and reasoning to design decision-support systems. Until now, these efforts have focused primarily on representing content of clinical guidelines and their logical structure. The present study aims to develop a computable representation of health-care providers' intentions to be used as part of a framework for implementing clinical decision-support systems. Our goal is to create an ontology that supports retrieval of plans based on the intentions or goals of the clinician. Methods: We developed an ontological representation of medical goals, plans, clinical scenarios and other relevant entities in medical decision-making. We used the resulting ontology along with an external ontology inference engine to simulate selection of clinical recommendations based on goals. The ontology instances used in the simulation were modeled from two clinical guidelines. Testing the design: Thirty-two clinical recommendations were encoded in the experimental model. Nine test cases were created to verify the ability of the model to retrieve the plans. For all nine cases, plans were successfully retrieved. Conclusion: The ontological design we developed supported effective reasoning over a medical knowledge base.(cont.) The immediate extension of this approach to be fully developed in medical applications may be partially limited by the lack of available editing tools. Many efforts in this area are currently aiming to the development of needed technologies.by Davide Zacacagnini [i.e. Zaccagnini].S.M

Probabilistic Graphical Modelling for Software Product Lines: A Frameweork for Modeling and Reasoning under Uncertainty

This work provides a holistic investigation into the realm of feature modeling within software product lines. The work presented identifies limitations and challenges within the current feature modeling approaches. Those limitations include, but not limited to, the dearth of satisfactory cognitive presentation, inconveniency in scalable systems, inflexibility in adapting changes, nonexistence of predictability of models behavior, as well as the lack of probabilistic quantification of model’s implications and decision support for reasoning under uncertainty. The work in this thesis addresses these challenges by proposing a series of solutions. The first solution is the construction of a Bayesian Belief Feature Model, which is a novel modeling approach capable of quantifying the uncertainty measures in model parameters by a means of incorporating probabilistic modeling with a conventional modeling approach. The Bayesian Belief feature model presents a new enhanced feature modeling approach in terms of truth quantification and visual expressiveness. The second solution takes into consideration the unclear support for the reasoning under the uncertainty process, and the challenging constraint satisfaction problem in software product lines. This has been done through the development of a mathematical reasoner, which was designed to satisfy the model constraints by considering probability weight for all involved parameters and quantify the actual implications of the problem constraints. The developed Uncertain Constraint Satisfaction Problem approach has been tested and validated through a set of designated experiments. Profoundly stating, the main contributions of this thesis include the following: • Develop a framework for probabilistic graphical modeling to build the purported Bayesian belief feature model. • Extend the model to enhance visual expressiveness throughout the integration of colour degree variation; in which the colour varies with respect to the predefined probabilistic weights. • Enhance the constraints satisfaction problem by the uncertainty measuring of the parameters truth assumption. • Validate the developed approach against different experimental settings to determine its functionality and performance