Tipo de dato abstracto para sistemas de bases de datos de tiempo real

En la actualidad, los Sistemas de Tiempo Real incorporan aplicaciones con uso intensivo de datos dentro del extenso espectro de soluciones en los cuales son aplicados. Un problema común a abordar por los desarrolladores, es el diseño orientado a datos con constricciones temporales, debido a que requiere considerar propiedades y reglas específicas para garantizar la validez de objetos respecto a instantes particulares de tiempo. Este trabajo propone un modelo para facilitar dicha tarea. Se presenta el concepto de dato de tiempo real con garantía de consistencia temporal y un conjunto de definiciones y clasificaciones asociadas. En base a esto, se modela un tipo de dato abstracto parametrizable que encapsula atributos y validaciones de constricciones temporales, de manera que el desarrollador de aplicaciones pueda abstraer y desligarse de esas responsabilidades. El resultado se verifica con la aplicación del modelo sobre un caso concreto de diseño, en un problema de informática industrial.Presentado en el II Workshop Procesamiento de Señales y Sistemas de Tiempo Real (WPSTR).Red de Universidades con Carreras en Informática (RedUNCI

Reflecting on the past and the present with temporal graph-based models

Self-adaptive systems (SAS) need to reflect on the current environment conditions, their past and current behaviour to support decision making. Decisions may have different effects depending on the context. On the one hand, some adaptations may have run into difficulties. On the other hand, users or operators may want to know why the system evolved in a certain direction. Users may just want to know why the system is showing a given behaviour or has made a decision as the behaviour may be surprising or not expected. We argue that answering emerging questions related to situations like these requires storing execution trace models in a way that allows for travelling back and forth in time, qualifying the decision making against available evidence. In this paper, we propose temporal graph databases as a useful representation for trace models to support self-explanation, interactive diagnosis or forensic analysis. We define a generic meta-model for structuring execution traces of SAS, and show how a sequence of traces can be turned into a temporal graph model. We present a first version of a query language for these temporal graphs through a case study, and outline the potential applications for forensic analysis (after the system has finished in a potentially abnormal way), self-explanation, and interactive diagnosis at runtime

A Temporal Web Ontology Language

The Web Ontology Language (OWL) is the most expressive standard language for modeling ontologies on the Semantic Web. In this paper, we present a temporal extension of the very expressive fragment SHIN(D) of the OWL-DL language resulting in the tOWL language. Through a layered approach we introduce 3 extensions: i) Concrete Domains, that allows the representation of restrictions using concrete domain binary predicates, ii) Temporal Representation, that introduces timepoints, relations between timepoints, intervals, and Allen’s 13 interval relations into the language, and iii) TimeSlices/Fluents, that implements a perdurantist view on individuals and allows for the representation of complex temporal aspects, such as process state transitions. We illustrate the expressiveness of the newly introduced language by providing a TBox representation of Leveraged Buy Out (LBO) processes in financial applications and an ABox representation of one specific LBO

Raising Time Awareness in Model-Driven Engineering

International audienceThe conviction that big data analytics is a key for the success of modern businesses is growing deeper, and the mo-bilisation of companies into adopting it becomes increasingly important. Big data integration projects enable companies to capture their relevant data, to efficiently store it, turn it into domain knowledge, and finally monetize it. In this context, historical data, also called temporal data, is becoming increasingly available and delivers means to analyse the history of applications, discover temporal patterns, and predict future trends. Despite the fact that most data that today's applications are dealing with is inherently temporal current approaches, methodologies, and environments for developing these applications don't provide sufficient support for handling time. We envision that Model-Driven Engineering (MDE) would be an appropriate ecosystem for a seamless and orthogonal integration of time into domain modelling and processing. In this paper, we investigate the state-of-the-art in MDE techniques and tools in order to identify the missing bricks for raising time-awareness in MDE and outline research directions in this emerging domain

Les versions dans les bases de données orientées objet : modélisation et manipulation

This thesis concerns object oriented databases; it proposes solutions to model and manage databases integrating versions. The concept of version is needed in various application fields such as technical documentation management, computer aided design and software engineering. Versions permit notably to keep and manage the evolution of the real world entities handled in such fields. There are different ways for versioning. Some works chose to describe the global evolution of a database; they manage versions of the whole database or versions of a database subpart. Our study focuses on representing independently the evolution of each entity described in the database. On the one hand, we propose a conceptual model extended to the versioning of objects and classes. Composition and relationship links, whose semantics are refined by cardinalities, integrate versioning for complex entities. Such links, including versions, induce complex contraints for structural integrity. On the other hand, we propose a language to manage this kind of databases. Particularly, this language provide a SelectFromWhere-type querying which take into account the specificities of versions; a query can take the most of the different abstraction levels related to versions that is to say derivation forests, trees and versions. The model and the language are realized within a prototype. This prototype is an end-user interface which provides a graphical management of databases integrating versions.Cette thèse s'inscrit dans le domaine des bases de données orientées objet ; elle propose des solutions pour décrire et manipuler des bases de données intégrant des versions. Le concept de version est nécessaire dans de nombreux domaines d'application comme la gestion de documentations techniques, la conception assistée par ordinateur et le génie logiciel. Les versions permettent notamment de conserver et manipuler l'évolution des entités du monde réel gérées dans de tels domaines. Différentes gestions de versions sont possibles. Certains travaux gèrent des versions de base ou d'une partie de base pour décrire l'évolution globale d'une base de données ; notre étude s'intéresse, quant à elle, à la représentation de l'évolution de chaque entité décrite dans la base, de manière indépendante. Nous proposons, d'une part, un modèle conceptuel intégrant la gestion de versions d'objets et de classes. Les relations de composition et d'association, dont la sémantique est affinée à l'aide de cardinalités, intègrent les versions pour des entités complexes. De telles relations, incluant les versions, induisent des contraintes d'intégrité structurelle complexes, dont nous faisons l'étude. D'autre part, nous proposons un langage pour manipuler ce type de bases de données. Ce langage permet notamment une interrogation de type Select From Where qui prend en compte les spécificités liées aux versions ; les différents niveaux d'abstraction liés aux versions c'est-à-dire les forêts de dérivation, les arbres et les versions, peuvent être exploités lors d'une interrogation. Une réalisation du modèle et du langage est effectuée au sein d'un prototype. Ce prototype est une interface destinée à des utilisateurs occasionnels, en permettant de manipuler graphiquement une base de données intégrant des versions

Enabling Model-Driven Live Analytics For Cyber-Physical Systems: The Case of Smart Grids

Advances in software, embedded computing, sensors, and networking technologies will lead to a new generation of smart cyber-physical systems that will far exceed the capabilities of today’s embedded systems. They will be entrusted with increasingly complex tasks like controlling electric grids or autonomously driving cars. These systems have the potential to lay the foundations for tomorrow’s critical infrastructures, to form the basis of emerging and future smart services, and to improve the quality of our everyday lives in many areas. In order to solve their tasks, they have to continuously monitor and collect data from physical processes, analyse this data, and make decisions based on it. Making smart decisions requires a deep understanding of the environment, internal state, and the impacts of actions. Such deep understanding relies on efficient data models to organise the sensed data and on advanced analytics. Considering that cyber-physical systems are controlling physical processes, decisions need to be taken very fast. This makes it necessary to analyse data in live, as opposed to conventional batch analytics. However, the complex nature combined with the massive amount of data generated by such systems impose fundamental challenges. While data in the context of cyber-physical systems has some similar characteristics as big data, it holds a particular complexity. This complexity results from the complicated physical phenomena described by this data, which makes it difficult to extract a model able to explain such data and its various multi-layered relationships. Existing solutions fail to provide sustainable mechanisms to analyse such data in live. This dissertation presents a novel approach, named model-driven live analytics. The main contribution of this thesis is a multi-dimensional graph data model that brings raw data, domain knowledge, and machine learning together in a single model, which can drive live analytic processes. This model is continuously updated with the sensed data and can be leveraged by live analytic processes to support decision-making of cyber-physical systems. The presented approach has been developed in collaboration with an industrial partner and, in form of a prototype, applied to the domain of smart grids. The addressed challenges are derived from this collaboration as a response to shortcomings in the current state of the art. More specifically, this dissertation provides solutions for the following challenges: First, data handled by cyber-physical systems is usually dynamic—data in motion as opposed to traditional data at rest—and changes frequently and at different paces. Analysing such data is challenging since data models usually can only represent a snapshot of a system at one specific point in time. A common approach consists in a discretisation, which regularly samples and stores such snapshots at specific timestamps to keep track of the history. Continuously changing data is then represented as a finite sequence of such snapshots. Such data representations would be very inefficient to analyse, since it would require to mine the snapshots, extract a relevant dataset, and finally analyse it. For this problem, this thesis presents a temporal graph data model and storage system, which consider time as a first-class property. A time-relative navigation concept enables to analyse frequently changing data very efficiently. Secondly, making sustainable decisions requires to anticipate what impacts certain actions would have. Considering complex cyber-physical systems, it can come to situations where hundreds or thousands of such hypothetical actions must be explored before a solid decision can be made. Every action leads to an independent alternative from where a set of other actions can be applied and so forth. Finding the sequence of actions that leads to the desired alternative, requires to efficiently create, represent, and analyse many different alternatives. Given that every alternative has its own history, this creates a very high combinatorial complexity of alternatives and histories, which is hard to analyse. To tackle this problem, this dissertation introduces a multi-dimensional graph data model (as an extension of the temporal graph data model) that enables to efficiently represent, store, and analyse many different alternatives in live. Thirdly, complex cyber-physical systems are often distributed, but to fulfil their tasks these systems typically need to share context information between computational entities. This requires analytic algorithms to reason over distributed data, which is a complex task since it relies on the aggregation and processing of various distributed and constantly changing data. To address this challenge, this dissertation proposes an approach to transparently distribute the presented multi-dimensional graph data model in a peer-to-peer manner and defines a stream processing concept to efficiently handle frequent changes. Fourthly, to meet future needs, cyber-physical systems need to become increasingly intelligent. To make smart decisions, these systems have to continuously refine behavioural models that are known at design time, with what can only be learned from live data. Machine learning algorithms can help to solve this unknown behaviour by extracting commonalities over massive datasets. Nevertheless, searching a coarse-grained common behaviour model can be very inaccurate for cyber-physical systems, which are composed of completely different entities with very different behaviour. For these systems, fine-grained learning can be significantly more accurate. However, modelling, structuring, and synchronising many fine-grained learning units is challenging. To tackle this, this thesis presents an approach to define reusable, chainable, and independently computable fine-grained learning units, which can be modelled together with and on the same level as domain data. This allows to weave machine learning directly into the presented multi-dimensional graph data model. In summary, this thesis provides an efficient multi-dimensional graph data model to enable live analytics of complex, frequently changing, and distributed data of cyber-physical systems. This model can significantly improve data analytics for such systems and empower cyber-physical systems to make smart decisions in live. The presented solutions combine and extend methods from model-driven engineering, [email protected], data analytics, database systems, and machine learning