<p>Event-triggered real-time systems are desirable to use in environments where the arrival of events are hard to predict. The semantics of an event-triggered system is well mapped to the behaviour of an active database management system (ADBMS), specified using event-condition-action (ECA) rules. The benefits of using an active database, such as persistent data storage, concurrency control, timely response to event occurrences etc. highlights the need for a development method for event-triggered real-time systems using active databases.</p><p>However, there are problems left to be solved before an ADBMS can be used with confidence in real-time environments. The behaviour of a real-time system must be predictable, which implies a thorough analysed specification with e.g. specified worst case execution times. The predictability requirement is an obstacle for specifying real-time systems as ECA rules, since the rules may affect each other in many intricate ways which makes them hard to analyse. The interaction between the rules implies that it is not enough to verify the correctness of single rules; an analysis must consider the behaviour of the entire rule set.</p><p>In this dissertation, an approach for developing active applications is presented. A method is examined which starts with an analysed high-level timed automaton specification and transforms the specified behaviour into an implicitly analysed rule set. For this method to be useful, the transformation from timed automata to rules must preserve the exact behaviour of the high level specification. Hence, the aim of this dissertation is to verify transformations between timed automaton specifications and ECA rules.</p><p>The contribution of this project is a structured set of general transformations between timed automata specifications and ECA rules. The transformations include both transformations of small timed automata constructs for deterministic environments and formally verified timed automata patterns specifying the behaviour of composite events in recent and chronicle context.</p>

This thesis describes a proposed design and implementation of a predictive maintenance engine developed to fulfill the requirements of the STO Company (Societe de transport de l'Outaouais) for maintaining vehicles in the fleet. Predictive maintenance is proven to be an effective approach and has become an industry standard in many fields. However, in the transportation industry, it is still in the stages of development due to the complexity of moving systems and the high level dimensions of involved parameters. Because it is almost impossible to cover all use cases of the vehicle operational process using one particular approach to predictive maintenance, in our work we take a systematic approach to designing a predictive maintenance system in several steps. Each step is implemented at the corresponding development stage based on the available data accumulated during system funсtioning cycle. % by dividing the entire system into modules and implementing different approaches. This thesis delves into the process of designing the general infrastructural model of the fleet management system (FMS), while focusing on the edge gateway module located on the vehicle and its function of detecting maintenance events based on current vehicle status. Several approaches may be used to detect maintenance events, such as a machine learning approach or an expert system-based approach. While the final version of fleet management system will use a hybrid approach, in this thesis paper we chose to focus on the second option based on expert knowledge, while machine learning has been left for future implementation since it requires extensive training data to be gathered prior to conducting experiments and actualizing operations. Inspired by the IDEA methodology which promotes mapping business rules as software classes and using the object-relational model for mapping objects to database entities, we take active database features as a base for developing a rule engine implementation. However, in contrast to the IDEA methodology which seeks to describe the specific system and its sub-modules, then build active rules based on the interaction between sub-systems, we are not aware of the functional structure of the vehicle due to its complexity. Instead, we develop a framework for creating specific active rules based on abstract classifications structured as ECA rules (event-condition-action), but with some expansions made due to the specifics of vehicle maintenance. The thesis describes an attempt to implement such a framework, and particularly the rule engine module, using active database features making it possible to encapsulate the active behaviour inside the database and decouple event detection from other functionalities. We provide the system with a set of example rules and then conduct a series of experiments analyzing the system for performance and correctness of events detection.

<p>Real-time systems are required to answer to external stimuli within a specified time-period. For this to be possible, the systems behaviour must be predictable. The use of active databases in real-time systems introduces unpredictability in the system, e.g. due to their use of active rules. The behaviour in active databases is usually specified in ECA-rules. Sets of ECA-rules are hard to analyse, which implies that the behaviour of the ECA-rule set is hard to predict.</p><p>The purpose of this project is to evaluate the ability to support the development of a predictable ECA-rule set. Using a formal method for the specification task is desirable, since a formal specification is analysable and can be proven correct. In this project, timed-automata are used for specifying the systems behaviour. A method for deriving predictable ECA-rules from a timed-automaton specification is developed, and successfully applied on a case-study specification. For this case-study specification, a set of ECA-rules preserving the analysed behaviour of the timed-automata specification is derived.</p>

Event-triggered real-time systems are desirable to use in environments where the arrival of events are hard to predict. The semantics of an event-triggered system is well mapped to the behaviour of an active database management system (ADBMS), specified using event-condition-action (ECA) rules. The benefits of using an active database, such as persistent data storage, concurrency control, timely response to event occurrences etc. highlights the need for a development method for event-triggered real-time systems using active databases. However, there are problems left to be solved before an ADBMS can be used with confidence in real-time environments. The behaviour of a real-time system must be predictable, which implies a thorough analysed specification with e.g. specified worst case execution times. The predictability requirement is an obstacle for specifying real-time systems as ECA rules, since the rules may affect each other in many intricate ways which makes them hard to analyse. The interaction between the rules implies that it is not enough to verify the correctness of single rules; an analysis must consider the behaviour of the entire rule set. In this dissertation, an approach for developing active applications is presented. A method is examined which starts with an analysed high-level timed automaton specification and transforms the specified behaviour into an implicitly analysed rule set. For this method to be useful, the transformation from timed automata to rules must preserve the exact behaviour of the high level specification. Hence, the aim of this dissertation is to verify transformations between timed automaton specifications and ECA rules. The contribution of this project is a structured set of general transformations between timed automata specifications and ECA rules. The transformations include both transformations of small timed automata constructs for deterministic environments and formally verified timed automata patterns specifying the behaviour of composite events in recent and chronicle context.

Real-time systems are required to answer to external stimuli within a specified time-period. For this to be possible, the systems behaviour must be predictable. The use of active databases in real-time systems introduces unpredictability in the system, e.g. due to their use of active rules. The behaviour in active databases is usually specified in ECA-rules. Sets of ECA-rules are hard to analyse, which implies that the behaviour of the ECA-rule set is hard to predict. The purpose of this project is to evaluate the ability to support the development of a predictable ECA-rule set. Using a formal method for the specification task is desirable, since a formal specification is analysable and can be proven correct. In this project, timed-automata are used for specifying the systems behaviour. A method for deriving predictable ECA-rules from a timed-automaton specification is developed, and successfully applied on a case-study specification. For this case-study specification, a set of ECA-rules preserving the analysed behaviour of the timed-automata specification is derived.

With the introduction of Wireless Multimedia Sensor Networks, large-scale remote outdoor surveillance applications where the majority of the cameras will be battery-operated are envisioned. These are the applications where the frequency of incidents is too low to employ permanent staffing such as monitoring of land and marine border, critical infrastructures, bridges, water supplies, etc. Given the inexpensive costs of wireless resource constrained camera sensors, the size of these networks will be significantly larger than the traditional multi-camera systems. While large number of cameras may increase the coverage of the network, such a large size along with resource constraints poses new challenges, e.g., localization, classification, tracking or reactive behavior. This dissertation proposes a framework that transforms current multi-camera networks into low-cost and reactive systems which can be used in large-scale remote surveillance applications. Specifically, a remote surveillance system framework with three components is proposed: 1) Localization and tracking of objects / 2) Classification and identification of objects / and 3) Reactive behavior at the base-station. For each component, novel lightweight, storage-efficient and real-time algorithms both at the computation and communication level are designed, implemented and tested under a variety of conditions. The results have indicated the feasibility of this framework working with limited energy but having high object localization/classification accuracies. The results of this research will facilitate the design and development of very large-scale remote border surveillance systems and improve the systems effectiveness in dealing with the intrusions with reduced human involvement and labor costs.

Un système d'aide à la décision (SIAD) est un système d'information qui assiste lesdécideurs impliqués dans les processus de décision complexes. Les SIAD modernesont besoin d'exploiter, en plus de données numériques et symboliques, des donnéeshétérogènes (données texte, données multimédia, ...) et provenant de sources diverses(comme le Web). Nous qualifions ces données complexes. Les entrepôts dedonnées forment habituellement le socle des SIAD. Ils permettent d'intégrer des données provenant de diverses sources pour appuyer le processus décisionnel. Cependant, l'avènement de données complexes impose une nouvelle vision de l'entreposagedes données, y compris de l'intégration des données, de leur stockage et de leuranalyse. En outre, les exigences d'aujourd'hui imposent l'intégration des donnéescomplexes presque en temps réel, pour remplacer le processus ETL traditionnel(Extraction, Transformation et chargement). Le traitement en temps réel exige unprocessus ETL plus actif. Les tâches d'intégration doivent réagir d'une façon intelligente, c'est-à-dire d'une façon active et autonome pour s'adapter aux changementsrencontrés dans l'environnement d'intégration des données, notamment au niveaudes sources de données.Dans cette thèse, nous proposons des solutions originales pour l'intégration dedonnées complexes en temps réel, de façon active et autonome. En eet, nous avons conçu une approche générique basé sur des métadonnées, orientée services et orienté évènements pour l'intégration des données complexes. Pour prendre en charge lacomplexité des données, notre approche stocke les données complexes à l'aide d'unformat unie en utilisant une approche base sur les métadonnées et XML. Nous noustraitons également la distribution de données et leur l'interopérabilité en utilisantune approche orientée services. Par ailleurs, pour considérer le temps réel, notreapproche stocke non seulement des données intégrées dans un référentiel unie,mais présente des fonctions d'intégration des données a la volée. Nous appliquonségalement une approche orientée services pour observer les changements de donnéespertinentes en temps réel. En outre, l'idée d'intégration des données complexes defaçon active et autonome, nous proposons une méthode de fouille dans les évènements.Pour cela, nous proposons un algorithme incrémentiel base sur XML pourla fouille des règles d'association a partir d’évènements. Ensuite, nous denissonsdes règles actives a l'aide des données provenant de la fouille d'évènements an deréactiver les tâches d'intégration.Pour valider notre approche d'intégration de données complexes, nous avons développé une plateforme logicielle, à savoir AX-InCoDa ((Active XML-based frameworkfor Integrating Complex Data). AX-InCoDa est une application Web implémenté à l'aide d'outils open source. Elle exploite les standards du Web (comme les services Web et XML) et le XML actif pour traiter la complexité et les exigences temps réel. Pour explorer les évènements stockés dans base d'évènement, nous avons proposons une méthode de fouille d'évènements an d'assurer leur autogestion.AX-InCoDa est enrichi de règles actives L'ecacite d'AX-InCoDa est illustrée par une étude de cas sur des données médicales. En, la performance de notre algorithme de fouille d'évènements est démontrée expérimentalement. / A decision support system (DSS) is an information system that supports decisionmakers involved in complex decision-making processes. Modern DSSs needto exploit data that are not only numerical or symbolic, but also heterogeneouslystructured (e.g., text and multimedia data) and coming from various sources (e.g,the Web). We term such data complex data. Data warehouses are casually usedas the basis of such DSSs. They help integrate data from a variety of sourcesto support decision-making. However, the advent of complex data imposes anothervision of data warehousing including data integration, data storage and dataanalysis. Moreover, today's requirements impose integrating complex data in nearreal-time rather than with traditional snapshot and batch ETL (Extraction, Transformationand Loading). Real-time and near real-time processing requires a moreactive ETL process. Data integration tasks must react in an intelligent, i.e., activeand autonomous way, to encountered changes in the data integration environment,especially data sources.In this dissertation, we propose novel solutions for complex data integration innear real-time, actively and autonomously. We indeed provide a generic metadatabased,service-oriented and event-driven approach for integrating complex data.To address data complexity issues, our approach stores heterogeneous data into aunied format using a metadata-based approach and XML. We also tackle datadistribution and interoperability using a service-oriented approach. Moreover, toaddress near real-time requirements, our approach stores not only integrated datainto a unied repository, but also functions to integrate data on-the-y. We also apply a service-oriented approach to track relevant data changes in near real-time.Furthermore, the idea of integrating complex data actively and autonomously revolvesaround mining logged events of data integration environment. For this sake,we propose an incremental XML-based algorithm for mining association rules fromlogged events. Then, we de ne active rules upon mined data to reactivate integrationtasks.To validate our approach for managing complex data integration, we develop ahigh-level software framework, namely AX-InCoDa (Active XML-based frameworkfor Integrating Complex Data). AX-InCoDa is implemented as Web application usingopen-source tools. It exploits Web standards (e.g., XML and Web services) andActive XML to handle complexity issues and near real-time requirements. Besidewarehousing logged events into an event repository to be mined for self-managingpurposes, AX-InCoDa is enriched with active rules. AX-InCoDa's feasibility is illustratedby a healthcare case study. Finally, the performance of our incremental eventmining algorithm is experimentally demonstrated.