A conceptual system design and managerial complexity competency model

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Complex adaptive systems are usually difficult to design and control. There are several particular methods for coping with complexity, but there is no general approach to build complex adaptive systems. The challenges of designing complex adaptive systems in a highly dynamic world drive the need for anticipatory capacity within engineering organizations, with a goal of enabling the design of systems that can cope with an unpredictable environment. This thesis explores this question of enhancing anticipatory capacity through the study of a complex adaptive system design methodology and complexity management competencies. A general introduction to challenges and issues in complex adaptive systems design is given, since a good understanding of the industrial context is considered necessary in order to avoid oversimplification of the problem, neglecting certain important factors and being unaware of important influences and relationships. In addition, a general introduction to complex thinking is given, since designing complex adaptive systems requires a non-classical thought, while practical notions of complexity theory and design are put forward. Building on these, the research proposes a Complex Systems Life-Cycle Understanding and Design (CXLUD) methodology to aid system architects and engineers in the design and control of complex adaptive systems. Starting from a creative anticipation construct - a loosening mechanism to allow for more options to be considered, the methodology proposes a conceptual framework and a series of stages to follow to find proper mechanisms that will promote elements to desired solutions by actively interacting among themselves. To illustrate the methodology, a financial systemic risks infrastructure systems architecture development case study is presented. The final part of this thesis develops a conceptual model to analyse managerial complexity competency model from a qualitative phenomenological study perspective. The model developed in this research is called Understanding-Perception-Action (UPA) managerial complexity competency model. The results of this competency model can be used to help ease project manager’s transition into complex adaptive projects, as well as serve as a foundation to launch qualitative and quantitative research into this area of project complexity management

Holland City News, Volume 86, Number 42: October 17, 1957

Newspaper published in Holland, Michigan, from 1872-1977, to serve the English-speaking people in Holland, Michigan. Purchased by local Dutch language newspaper, De Grondwet, owner in 1888.https://digitalcommons.hope.edu/hcn_1957/1041/thumbnail.jp

SeMoM: a semantic middleware for IoT healthcare applications

De nos jours, l'internet des objets (IoT) connaît un intérêt considérable tant de la part du milieu universitaire que de l'industrie. Il a contribué à améliorer la qualité de vie, la croissance des entreprises et l'efficacité dans de multiples domaines. Cependant, l'hétérogénéité des objets qui peuvent être connectés dans de tels environnements, rend difficile leur interopérabilité. En outre, les observations produites par ces objets sont générées avec différents vocabulaires et formats de données. Cette hétérogénéité de technologies dans le monde IoT rend nécessaire l'adoption de solutions génériques à l'échelle mondiale. De plus, elle rend difficile le partage et la réutilisation des données dans d'autres buts que ceux pour lesquels elles ont été initialement mises en place. Dans cette thèse, nous abordons ces défis dans le contexte des applications de santé. Pour cela, nous proposons de transformer les données brutes issues de capteurs en connaissances et en informations en s'appuyant sur les ontologies. Ces connaissances vont être partagées entre les différents composants du système IoT. En ce qui concerne les défis d'hétérogénéité et d'interopérabilité, notre contribution principale est une architecture IoT utilisant des ontologies pour permettre le déploiement d'applications IoT sémantiques. Cette approche permet de partager les observations des capteurs, la contextualisation des données et la réutilisation des connaissances et des informations traitées. Les contributions spécifiques comprennent : * Conception d'une ontologie " Cognitive Semantic Sensor Network ontology (CoSSN) " : Cette ontologie vise à surmonter les défis d'interopérabilité sémantiques introduits par la variété des capteurs potentiellement utilisés. CoSSN permet aussi de modéliser la représentation des connaissances des experts. * Conception et mise en œuvre de SeMoM: SeMoM est une architecture flexible pour l'IoT intégrant l'ontologie CoSSN. Elle s'appuie sur un middleware orienté message (MoM) pour offrir une solution à couplage faible entre les composants du système. Ceux-ci peuvent échanger des données d'observation sémantiques de manière flexible à l'aide du paradigme producteur/consommateur. Du point de vue applicatif, nous sommes intéressés aux applications de santé. Dans ce domaine, les approches spécifiques et les prototypes individuels sont des solutions prédominantes ce qui rend difficile la collaboration entre différentes applications, en particulier dans un cas de patients multi-pathologies. En ce qui concerne ces défis, nous nous sommes intéressés à deux études de cas: 1) la détection du risque de développement des escarres chez les personnes âgées et 2) la détection des activités de la vie quotidienne (ADL) de personnes pour le suivi et l'assistance à domicile : * Nous avons développé des extensions de CoSSN pour décrire chaque concept en lien avec les deux cas d'utilisation. Nous avons également développé des applications spécifiques grâce à SeMoM qui mettent en œuvre des règles de connaissances expertes permettant d'évaluer et de détecter les escarres et les activités. * Nous avons mis en œuvre et évaluer le framework SeMoM en se basant sur deux expérimentations. La première basée sur le déploiement d'un système ciblant la détection des activités ADL dans un laboratoire d'expérimentation pour la santé (le Connected Health Lab). La seconde est basée sur le simulateur d'activités ADLSim développé par l'Université d'Oslo. Ce simulateur a été utilisé pour effectuer des tests de performances de notre solution en générant une quantité massive de données sur les activités d'une personne à domicile.Nowadays, the adoption of the Internet of Things (IoT) has received a considerable interest from both academia and industry. It provides enhancements in quality of life, business growth and efficiency in multiple domains. However, the heterogeneity of the "Things" that can be connected in such environments makes interoperability among them a challenging problem. Moreover, the observations produced by these "Things" are made available with heterogeneous vocabularies and data formats. This heterogeneity prevents generic solutions from being adopted on a global scale and makes difficult to share and reuse data for other purposes than those for which they were originally set up. In this thesis, we address these challenges in the context of healthcare applications considering how we transform raw data to cognitive knowledge and ontology-based information shared between IoT system components. With respect to heterogeneity and integration challenges, our main contribution is an ontology-based IoT architecture allowing the deployment of semantic IoT applications. This approach allows sharing of sensors observations, contextualization of data and reusability of knowledge and processed information. Specific contributions include: * Design of the Cognitive Semantic Sensor Network ontology (CoSSN) ontology: CoSSN aims at overcoming the semantic interoperability challenges introduced by the variety of sensors potentially used. It also aims at describing expert knowledge related to a specific domain. * Design and implementation of SeMoM: SeMoM is a flexible IoT architecture built on top of CoSSN ontology. It relies on a message oriented middleware (MoM) following the publish/subscribe paradigm for a loosely coupled communication between system components that can exchange semantic observation data in a flexible way. From the applicative perspective, we focus on healthcare applications. Indeed, specific approaches and individual prototypes are preeminent solutions in healthcare which straighten the need of an interoperable solution especially for patients with multiple affections. With respect to these challenges, we elaborated two case studies 1) bedsore risk detection and 2) Activities of Daily Living (ADL) detection as follows: * We developed extensions of CoSSN to describe each domain concepts and we developed specific applications through SeMoM implementing expert knowledge rules and assessments of bedsore and human activities. * We implemented and evaluated the SeMoM framework in order to provide a proof of concept of our approach. Two experimentations have been realized for that target. The first is based on a deployment of a system targeting the detection of ADL activities in a real smart platform. The other one is based on ADLSim, a simulator of activities for ambient assisted living that can generate a massive amount of data related to the activities of a monitored person

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section

Internet of Things From Hype to Reality

The Internet of Things (IoT) has gained significant mindshare, let alone attention, in academia and the industry especially over the past few years. The reasons behind this interest are the potential capabilities that IoT promises to offer. On the personal level, it paints a picture of a future world where all the things in our ambient environment are connected to the Internet and seamlessly communicate with each other to operate intelligently. The ultimate goal is to enable objects around us to efficiently sense our surroundings, inexpensively communicate, and ultimately create a better environment for us: one where everyday objects act based on what we need and like without explicit instructions

A Comprehensive Study of Declarative Modelling Languages

Declarative behavioural modelling is a powerful modelling paradigm that enables users to model system functionality abstractly and formally. An abstract model is a concise and compact representation of key characteristics of a system, and enables the stakeholders to reason about the correctness of the system in the early stages of development. There are many different declarative languages and they have greatly varying constructs for representing a transition system, and they sometimes differ in rather subtle ways. In this thesis, we compare seven formal declarative modelling languages B, Event-B, Alloy, Dash, TLA+, PlusCal, and AsmetaL on several criteria. We classify these criteria under three main categories: structuring transition systems (control modelling), data descriptions in transition systems (data modelling), and modularity aspects of modelling. We developed this comparison by completing a set of case studies across the data- vs. control-oriented spectrum in all of the above languages. Structurally, a transition system is comprised of a snapshot declaration and snapshot space, initialization, and a transition relation, which is potentially composed of individual transitions. We meticulously outline the differences between the languages with respect to how the modeller would express each of the above components of a transition system in each language, and include discussions regarding stuttering and inconsistencies in the transition relation. Data-related aspects of a formal model include use of basic and composite datatypes, well-formedness and typechecking, and separation of name spaces with respect to global and local variables. Modularity criteria includes subtransition systems and data decomposition. We employ a series of small and concise exemplars we have devised to highlight these differences in each language. To help modellers answer the important question of which declarative modelling language may be most suited for modelling their system, we present recommendations based on our observations about the differentiating characteristics of each of these languages

Profiling Alloy Models

Modeling of software-intensive systems using formal declarative modeling languages offers a means of managing software complexity through the use of abstraction and early identification of correctness issues by formal analysis. Alloy is one such language used for modeling systems early in the development process. Nevertheless, little work has been done to study the styles and techniques commonly used in Alloy models. We present the first static analysis study of Alloy models. We investigate research questions that examine a large corpus of 2,138 Alloy models. To evaluate these research questions, we create a methodology that leverages the power of ANTLR pattern matching and the query language XPath. We investigate the parse tree generated from each Alloy model and identify instances of formulated queries that are of interest to our research questions. We present the results and discuss the findings from examining these research questions. Our research questions are split into three categories depending on their purpose and implementation complexity. Characteristics of Models include ``surface-level" research questions that aim to identify what language constructs are used commonly. We also correlate certain model features using linear regression to determine the best predictors for model length and field count. Patterns of Use questions are considerably more complex and attempt to identify how modelers are using Alloy's constructs. Analysis Complexity questions explore the use of Alloy model features and constructs that may impact solving time. We draw conclusions from the results of our research questions and present findings for language and tool designers, educators and optimization developers. Findings aimed at language and tool designers present ways to improve the Alloy language by adding constructs or removing unused ones based on trends identified in our corpus of models. Findings for educators are intended to highlight underutilized language constructs and features, and help student modelers avoid discouraged practices. Lastly, we present a number of findings for optimization developers that provide suggestions for back-end improvements