Using spatiotemporal patterns to qualitatively represent and manage dynamic situations of interest : a cognitive and integrative approach

Les situations spatio-temporelles dynamiques sont des situations qui évoluent dans l’espace et dans le temps. L’être humain peut identifier des configurations de situations dans son environnement et les utilise pour prendre des décisions. Ces configurations de situations peuvent aussi être appelées « situations d’intérêt » ou encore « patrons spatio-temporels ». En informatique, les situations sont obtenues par des systèmes d’acquisition de données souvent présents dans diverses industries grâce aux récents développements technologiques et qui génèrent des bases de données de plus en plus volumineuses. On relève un problème important dans la littérature lié au fait que les formalismes de représentation utilisés sont souvent incapables de représenter des phénomènes spatiotemporels dynamiques et complexes qui reflètent la réalité. De plus, ils ne prennent pas en considération l’appréhension cognitive (modèle mental) que l’humain peut avoir de son environnement. Ces facteurs rendent difficile la mise en œuvre de tels modèles par des agents logiciels. Dans cette thèse, nous proposons un nouveau modèle de représentation des situations d’intérêt s’appuyant sur la notion des patrons spatiotemporels. Notre approche utilise les graphes conceptuels pour offrir un aspect qualitatif au modèle de représentation. Le modèle se base sur les notions d’événement et d’état pour représenter des phénomènes spatiotemporels dynamiques. Il intègre la notion de contexte pour permettre aux agents logiciels de raisonner avec les instances de patrons détectés. Nous proposons aussi un outil de génération automatisée des relations qualitatives de proximité spatiale en utilisant un classificateur flou. Finalement, nous proposons une plateforme de gestion des patrons spatiotemporels pour faciliter l’intégration de notre modèle dans des applications industrielles réelles. Ainsi, les contributions principales de notre travail sont : Un formalisme de représentation qualitative des situations spatiotemporelles dynamiques en utilisant des graphes conceptuels. ; Une approche cognitive pour la définition des patrons spatio-temporels basée sur l’intégration de l’information contextuelle. ; Un outil de génération automatique des relations spatiales qualitatives de proximité basé sur les classificateurs neuronaux flous. ; Une plateforme de gestion et de détection des patrons spatiotemporels basée sur l’extension d’un moteur de traitement des événements complexes (Complex Event Processing).Dynamic spatiotemporal situations are situations that evolve in space and time. They are part of humans’ daily life. One can be interested in a configuration of situations occurred in the environment and can use it to make decisions. In the literature, such configurations are referred to as “situations of interests” or “spatiotemporal patterns”. In Computer Science, dynamic situations are generated by large scale data acquisition systems which are deployed everywhere thanks to recent technological advances. Spatiotemporal pattern representation is a research subject which gained a lot of attraction from two main research areas. In spatiotemporal analysis, various works extended query languages to represent patterns and to query them from voluminous databases. In Artificial Intelligence, predicate-based models represent spatiotemporal patterns and detect their instances using rule-based mechanisms. Both approaches suffer several shortcomings. For example, they do not allow for representing dynamic and complex spatiotemporal phenomena due to their limited expressiveness. Furthermore, they do not take into account the human’s mental model of the environment in their representation formalisms. This limits the potential of building agent-based solutions to reason about these patterns. In this thesis, we propose a novel approach to represent situations of interest using the concept of spatiotemporal patterns. We use Conceptual Graphs to offer a qualitative representation model of these patterns. Our model is based on the concepts of spatiotemporal events and states to represent dynamic spatiotemporal phenomena. It also incorporates contextual information in order to facilitate building the knowledge base of software agents. Besides, we propose an intelligent proximity tool based on a neuro-fuzzy classifier to support qualitative spatial relations in the pattern model. Finally, we propose a framework to manage spatiotemporal patterns in order to facilitate the integration of our pattern representation model to existing applications in the industry. The main contributions of this thesis are as follows: A qualitative approach to model dynamic spatiotemporal situations of interest using Conceptual Graphs. ; A cognitive approach to represent spatiotemporal patterns by integrating contextual information. ; An automated tool to generate qualitative spatial proximity relations based on a neuro-fuzzy classifier. ; A platform for detection and management of spatiotemporal patterns using an extension of a Complex Event Processing engine

Evaluation of a fuzzy-expert system for fault diagnosis in power systems

A major problem with alarm processing and fault diagnosis in power systems is the reliance on the circuit alarm status. If there is too much information available and the time of arrival of the information is random due to weather conditions etc., the alarm activity is not easily interpreted by system operators. In respect of these problems, this thesis sets out the work that has been carried out to design and evaluate a diagnostic tool which assists power system operators during a heavy period of alarm activity in condition monitoring. The aim of employing this diagnostic tool is to monitor and raise uncertain alarm information for the system operators, which serves a proposed solution for restoring such faults. The diagnostic system uses elements of AI namely expert systems, and fuzzy logic that incorporate abductive reasoning. The objective of employing abductive reasoning is to optimise an interpretation of Supervisory Control and Data Acquisition (SCADA) based uncertain messages when the SCADA based messages are not satisfied with simple logic alone. The method consists of object-oriented programming, which demonstrates reusability, polymorphism, and readability. The principle behind employing objectoriented techniques is to provide better insights and solutions compared to conventional artificial intelligence (Al) programming languages. The characteristics of this work involve the development and evaluation of a fuzzy-expert system which tries to optimise the uncertainty in the 16-lines 12-bus sample power system. The performance of employing this diagnostic tool is assessed based on consistent data acquisition, readability, adaptability, and maintainability on a PC. This diagnostic tool enables operators to control and present more appropriate interpretations effectively rather than a mathematical based precise fault identification when the mathematical modelling fails and the period of alarm activity is high. This research contributes to the field of power system control, in particular Scottish Hydro-Electric PLC has shown interest and supplied all the necessary information and data. The AI based power system is presented as a sample application of Scottish Hydro-Electric and KEPCO (Korea Electric Power Corporation)

An adaptable fuzzy-based model for predicting link quality in robot networks.

It is often essential for robots to maintain wireless connectivity with other systems so that commands, sensor data, and other situational information can be exchanged. Unfortunately, maintaining sufficient connection quality between these systems can be problematic. Robot mobility, combined with the attenuation and rapid dynamics associated with radio wave propagation, can cause frequent link quality (LQ) issues such as degraded throughput, temporary disconnects, or even link failure. In order to proactively mitigate such problems, robots must possess the capability, at the application layer, to gauge the quality of their wireless connections. However, many of the existing approaches lack adaptability or the framework necessary to rapidly build and sustain an accurate LQ prediction model. The primary contribution of this dissertation is the introduction of a novel way of blending machine learning with fuzzy logic so that an adaptable, yet intuitive LQ prediction model can be formed. Another significant contribution includes the evaluation of a unique active and incremental learning framework for quickly constructing and maintaining prediction models in robot networks with minimal sampling overhead

Monitoring Complex Processes to Verify System Conformance: A Declarative Rule-Based Framework

Over the last 60 years, computers and software have favoured incredible advancements in every field. Nowadays, however, these systems are so complicated that it is difficult – if not challenging – to understand whether they meet some requirement or are able to show some desired behaviour or property. This dissertation introduces a Just-In-Time (JIT) a posteriori approach to perform the conformance check to identify any deviation from the desired behaviour as soon as possible, and possibly apply some corrections. The declarative framework that implements our approach – entirely developed on the promising open source forward-chaining Production Rule System (PRS) named Drools – consists of three components: 1. a monitoring module based on a novel, efficient implementation of Event Calculus (EC), 2. a general purpose hybrid reasoning module (the first of its genre) merging temporal, semantic, fuzzy and rule-based reasoning, 3. a logic formalism based on the concept of expectations introducing Event-Condition-Expectation rules (ECE-rules) to assess the global conformance of a system. The framework is also accompanied by an optional module that provides Probabilistic Inductive Logic Programming (PILP). By shifting the conformance check from after execution to just in time, this approach combines the advantages of many a posteriori and a priori methods proposed in literature. Quite remarkably, if the corrective actions are explicitly given, the reactive nature of this methodology allows to reconcile any deviations from the desired behaviour as soon as it is detected. In conclusion, the proposed methodology brings some advancements to solve the problem of the conformance checking, helping to fill the gap between humans and the increasingly complex technology.Negli ultimi 60 anni, i computer e i programmi hanno favorito incredibili avanzamenti in ogni campo. Oggigiorno, purtroppo, questi sistemi sono così complicati che è difficile – se non impossibile – capire se soddisfano qualche requisito o mostrano un comportamento o una proprietà desiderati. Questa tesi introduce un approccio a posteriori Just-In-Time (JIT) per effettuare il controllo di conformità ed identificare appena possibile ogni deviazione dal comportamento desiderato, ed eventualmente applicare qualche correzione. Il framework dichiarativo che implementa il nostro approccio – interamente sviluppato su una promettente piattaforma open source di Production Rule System (PRS) chiamata Drools – si compone di tre elementi: 1. un modulo per il monitoraggio basato su una nuova implementazione efficiente di Event Calculus (EC), 2. un modulo generale per il ragionamento ibrido (il primo del suo genere) che supporta ragionamento temporale, semantico, fuzzy e a regole, 3. un formalismo logico basato sul concetto di aspettativa che introduce le Event-Condition-Expectation rules (ECE-rules) per valutare la conformità globale di un sistema. Il framework è anche accompagnato da un modulo opzionale che fornisce Probabilistic Inductive Logic Programming (PILP). Spostando il controllo di conformità da dopo l’esecuzione ad appena in tempo, questo approccio combina i vantaggi di molti metodi a posteriori e a priori proposti in letteratura. Si noti che, se le azioni correttive sono fornite esplicitamente, la natura reattiva di questo metodo consente di conciliare le deviazioni dal comportamento desiderato non appena questo viene rilevato. In conclusione, la metodologia proposta introduce alcuni avanzamenti per risolvere il problema del controllo di conformità, contribuendo a colmare il divario tra l’uomo e la tecnologia, sempre più complessa

Fuzzy Logic

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems

Integrating requirements prioritization and selection into goal models

Requirements engineering is the first main activity in software development process. It must address the individual goals of the organization. The inadequate, inconsistent, incomplete and ambiguous requirements are main obstacles on the quality of software systems. Goal Oriented Requirements Engineering (GORE) starts with abstracts high level goals. These goals are refined to lower levels until they are assignable to agents. During GORE analysis, decisions need to be made among alternatives at various positions. Decisions involve different stakeholders which may contradict with each other based on certain criteria. In the context of GORE, the support for identifying and managing the criteria for requirements selection process is required. The criteria are based on stakeholders needs and preferences and therefore stakeholders opinions need to be involved in selection process. It helps to identify the importance of requirement according to stakeholders understandings and needs. It also helps in the understanding of interaction between system and stakeholders (stakeholders involvement in making important decisions) and by documenting the stakeholder preferences early in GORE, helps to identify inconsistencies early in the requirements engineering. Software quality requirements are essential part for the success of software development. Defined and guaranteed quality in software development requires identifying, refining, and predicting quality properties by appropriate means. Goal models and quality models are useful for modelling of functional goals as well as for quality goals. This thesis presents the integration of goal models with quality models, which helps to involve stakeholders opinions and the representation of dependencies among goals and quality models. The integration of goal models and quality models helps in the derivation of customized quality models. The integrated goal-quality model representing the functional requirements and quality requirements is used to rank each functional requirement arising from functional goals and quality requirement arising from quality goals. Triangular Fuzzy Numbers (TFN) are used to represent stakeholder opinions for prioritizing requirements. By defuzzification process on TFN, stakeholders opinions are quantified. TFN and defuzzification process is also used to prioritize the identified relationships among functional and non-functional requirements. In the last step development constraints are used to re-prioritize the requirements. After final prioritization, a selection algorithm helps to select the requirements based on benefit over cost ratio. The algorithm makes sure that maximum number of requirements are selected while fulfilling the upper cost limit. Thus the whole process helps in the selection of requirements based on stakeholders opinions, goal-quality models interaction and development constraints. The thesis also presents an integrative model of influence factors to tailor product line development processes according to different project needs, organizational goals, individual goals of the developers or constraints of the environment. Tailoring is realized with prioritized attributes, with which the resulting elements of the product, process and project analysed are ranked. An integrative model for the description of stakeholder needs and goals in relation to the development process artefacts and the development environment specifics is needed, to be able to analyse potential influences of changing goals early in the project development. The proposed tailoring meta-model includes goal models, SPEM models and requirements to development processes. With this model stakeholder specific goals can be used to support binding a variable part of the development process. This support addresses soft factors as well as concrete requirements.Requirements Engineering ist der erste Schritt im Softwareentwicklungsprozess. Er dient zur Aufnahme organisationsabhängiger Ziele und Anforderungen. Unangemessene, inkonsistente, unvollständige oder mehrdeutige Anforderungen können die Qualität von Softwaresystem stark negativ beeinflussen. Goal Oriented Requirements Engineering (GORE) beginnt mit der Entwicklung von übergeordneter Zielen, welche in weiteren Entwicklungsstufen verfeinert werden, bis sie einer verantwortlichen Person zugewiesen werden können. Während einer GORE Analyse werden an verschiedenen Stellen Entscheidungen über Alternativen getroffen. Diese Entscheidungen betreffen unterschiedliche Akteure, die sich in ihren Ansichten widersprechen können. Im Rahmen von GORE wird die Unterstützung zur Identifizierung und Verwaltung von Kriterien zur Auswahl von Anforderungen benötigt. Diese Kriterien basieren auf den Vorstellungen und Vorlieben von Stakeholdern, daher ist eine Integration aller Stakeholder in den Auswahlprozess erforderlich. Dies soll dabei helfen, die Bedeutung bestimmter Anforderungen auf Basis der betroffenen Personen zu identifizieren und aufzuarbeiten. Darüber hinaus hilft GORE bei der Kommunikation zwischen System und Akteuren durch ihren Einbezug in wichtige Entscheidungen. Durch frühzeitige Dokumentation des tatsächlichen Stakholderbedarfs können Inkonsistenzen im Requirements Engineering frühzeitig ermittelt werden. Die Bestimmung von Software Qualitätsmerkmalen ist wesentlicher Erfolgsfaktor in der Software Entwicklung. Zur Gewährleistung einer qualitativen Softwareentwicklung und eines entsprechenden Produktes sind die Identifizierung, die Verfeinerung und die Vorhersage von Qualitätseigenschaften jederzeit durch geeignete Maßnahmen erforderlich. Goal Models und Quality Models sind wertvolle Werkzeuge zur Ermittlung und Modellierung funktionaler und nicht-funktionaler Anforderungen und Ziele. Diese Arbeit enthält einen Lösungsansatz zur Integration von Goal Models und Quality Models, der dazu beitragen soll, Stakeholder und Abhängigkeiten zwischen Goal und Quality Models einzubeziehen und sichtbar zu machen. Die Integration von Goal Models und Quality Models soll zur Ableitung spezifischer Quality Models beitragen. Somit kann das integrierte Goal-Quality Model, welches die funktionalen Anforderungen und die Qualitätsanforderungen vereint, zur Priorisierung aller funktionalen Anforderung, die sich aus den funktionalen Zielen ergeben, und aller Qualitätsanforderungen, die aus Qualitätszielen resultieren, dienen. Zur Priorisierung der Anforderung auf Basis der Stakeholderbedarfe werden Triangular Fuzzy Numbers (TFN) verwendet. Nach der endgültigen Priorisierung dient ein spezieller Algorithmus zur Einschätzung und Auswahl der Anforderungen auf Basis einer Kosten-Nutzen-Analyse. Dieser Algorithmus stellt sicher, dass unter Einhaltung einer von der Organisation gewählten Kostenobergrenze die maximale Anzahl der Anforderungen umgesetzt werden kann. Der gesamte Prozess dient demnach zur Anforderungsanalyse unter Berücksichtigung verschiedener Interessengruppen, Abhängigkeiten, sowie durch den Einbezug von Grenzen, die sich beim Zusammenspiel von Goal-Quality Models und der Softwareentwicklung ergeben können. Darüber hinaus enthält die Arbeit ein integratives Modell, um Entwicklungsprozesse während der Erstellung von Produktlinien an Einflussfaktoren, wie Projektbedürfnisse, Organisationsziele, individuelle Ziele von Entwicklern oder an Umweltbedingungen anzupassen. Dieses sogenannte Tailoring wird durch Priorisierung von Attributen erreicht, welche verschiedene Elemente des zu erzeugende Produktes, des Prozesses oder des Projektes analysieren und nach Bedeutung sortieren. Ein integratives Modell zur Beschreibung von Stakeholderbedürfnissen und -zielen in Bezug auf die Artefakte des Entwicklungsprozesses und die Besonderheiten einer Entwicklungsumgebung wird benötigt, um potenzielle Einflüsse sich verändernder Ziele frühzeitig während der Projektentwicklung zu analysieren. Das hier vorgestellte Tailoring-Meta-Model beinhaltet Goal-Models, SPEM Models und Requirements hinsichtlich Entwicklungsprozesse. Mithilfe dieses Modells können stakeholderspezifische Ziele dazu verwendet werden, um einen variablen Teil eines Entwicklungsprozesses projektbezogen zu gestalten. Auf diese Weise können weiche Faktoren genauso integriert werden, wie konkrete Anforderungen

Harnessing Knowledge, Innovation and Competence in Engineering of Mission Critical Systems

This book explores the critical role of acquisition, application, enhancement, and management of knowledge and human competence in the context of the largely digital and data/information dominated modern world. Whilst humanity owes much of its achievements to the distinct capability to learn from observation, analyse data, gain insights, and perceive beyond original realities, the systematic treatment of knowledge as a core capability and driver of success has largely remained the forte of pedagogy. In an increasingly intertwined global community faced with existential challenges and risks, the significance of knowledge creation, innovation, and systematic understanding and treatment of human competence is likely to be humanity's greatest weapon against adversity. This book was conceived to inform the decision makers and practitioners about the best practice pertinent to many disciplines and sectors. The chapters fall into three broad categories to guide the readers to gain insight from generic fundamentals to discipline-specific case studies and of the latest practice in knowledge and competence management

Harnessing Knowledge, Innovation and Competence in Engineering of Mission Critical Systems

This book explores the critical role of acquisition, application, enhancement, and management of knowledge and human competence in the context of the largely digital and data/information dominated modern world. Whilst humanity owes much of its achievements to the distinct capability to learn from observation, analyse data, gain insights, and perceive beyond original realities, the systematic treatment of knowledge as a core capability and driver of success has largely remained the forte of pedagogy. In an increasingly intertwined global community faced with existential challenges and risks, the significance of knowledge creation, innovation, and systematic understanding and treatment of human competence is likely to be humanity's greatest weapon against adversity. This book was conceived to inform the decision makers and practitioners about the best practice pertinent to many disciplines and sectors. The chapters fall into three broad categories to guide the readers to gain insight from generic fundamentals to discipline-specific case studies and of the latest practice in knowledge and competence management

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation