A Return on Our Experience of Modeling a Service-oriented Organization in a Service Cartography

We present a longitudinal project using action design research, which is a four-year collaboration between two EPFL entities: The research Laboratory for Systemic Modeling (LAMS) and EPFL’s IT department, called the VPSI. During that time the VPSI was going through a transformation into a service-oriented organization. The research project began as an open-ended modeling of some of the VPSI processes. It slowly matured into the design and development of a visualization tool we call service cartography. During this research, we learned that, to successfully apply service-orientation, focusing purely on IT architecture and end-customer value is not enough. Attention must be given to the exchange of internal services between the service organization members and their alignment with the services expected by the external stakeholders. In this paper we present the evolution of (1) our understanding of what services are, and (2) our conceptualization of how the service cartography facilitates the service-oriented thinking

MusA: Using Indoor Positioning and Navigation to Enhance Cultural Experiences in a museum

In recent years there has been a growing interest into the use of multimedia mobile guides in museum environments. Mobile devices have the capabilities to detect the user context and to provide pieces of information suitable to help visitors discovering and following the logical and emotional connections that develop during the visit. In this scenario, location based services (LBS) currently represent an asset, and the choice of the technology to determine users' position, combined with the definition of methods that can effectively convey information, become key issues in the design process. In this work, we present MusA (Museum Assistant), a general framework for the development of multimedia interactive guides for mobile devices. Its main feature is a vision-based indoor positioning system that allows the provision of several LBS, from way-finding to the contextualized communication of cultural contents, aimed at providing a meaningful exploration of exhibits according to visitors' personal interest and curiosity. Starting from the thorough description of the system architecture, the article presents the implementation of two mobile guides, developed to respectively address adults and children, and discusses the evaluation of the user experience and the visitors' appreciation of these application

Caractérisation et logique d'une situation collaborative

Initié en 2009, le projet MISE 2.0 (deuxième itération du projet Mediation Information System Engineering) s’articule autour d’une approche BPM (pour Business Process Management) et d’une vision MDE (pour Model-Driven Engineering). La réalisation d’une démarche BPM classique au sein d’une organisation nécessite de recueillir une connaissance couvrant à la fois les aspects structurel, informationnel et fonctionnel afin de définir des modèles de processus caractéristiques du comportement de l’organisation. Concernant le projet MISE 2.0, l’approche BPM considérée concerne un ensemble d’organisations collaboratives. Quant à la composante MDE, elle est destinée à faciliter l’automatisation des différentes étapes de la démarche : i) Recueil de la connaissance (caractérisation de la situation) : Il s’agit de collecter les information concernant la situation collaborative considérée, ii) Déduction de la cartographie de processus collaboratifs (définition de la solution) : il s’agit de définit les processus collaboratifs adaptés à la situation collaboratives caractérisée au niveau précedent and iii) Déploiement du SI de médiation (implémentation de la solution) : il s’agit d’implémenter le SI de médiation sous la forme d’une plateforme informatique capable d’orchestrer les processus collaboratif définis. La problématique scientifique relève des deux transitions entre ces trois niveaux d’abstractions : la première transition est prise en charge au niveau abstrait de la démarche MISE 2.0 alors que la seconde est traitée au niveau concret. Les travaux de thèse dont il est ici question se focalisent sur le niveau abstrait : déduction d’une cartographie de processus collaboratifs satisfaisant la situation collaborative considérée. Ce type d’objectif relève généralement d’activités entièrement manuelles qui nécessitent une importante quantité de travail afin d’obtenir les modèles de processus escomptés. Les travaux de recherches présentés ambitionnent d’automatiser cette démarche. Le principe est le suivant : (i) recueil, sous la forme de modèles, de la connaissance nécessaire à la caractérisation de la situation collaborative (informations sur les partenaires, les fonctions qu’ils partagent et leurs objectifs), (ii) déduction de la connaissance complémentaire relative à la dynamique collaborative qui pourrait satisfaire ces objectifs selon les moyens disponibles (cette phase s’appuie sur un métamodèle collaboratif, sur l’ontologie associée et sur des règles de transformation) et (iii) structuration de cette connaissance générée sous la forme d’une cartographie de processus collaboratifs (grâce à des algorithmes dédiés). ABSTRACT : MISE 2.0 (for Mediation Information System Engineering, second iteration) project has been launched in 2009. The MISE 2.0 engineering approach is based on BPM (Business Process Management) and MDE (Model-Driven Engineering). Running a regular BPM approach on a specific organization consists in gathering structural, informational, and functional knowledge in order to design cartography of processes covering the behavior of the modeled organization. Regarding the MISE 2.0 project the BPM approach concerns a set of organizations and MDE helps in automatizing the different steps: i) Knowledge gathering (situation layer): collect information concerning the collaborative situation, ii) Processes cartography design (solution layer): design the processes according to the knowledge gathered and iii) MIS deployment (implementation layer): implement an IT structure able to run the processes cartography. Both the transitions between these layers are the hard-points of this approach: The first gap is managed at the abstract level of MISE 2.0 while the second one is managed at the concrete level of MISE 2.0. The current PhD is focused on the first issue: designing a relevant processes cartography from the modeled collaborative situation. However, this is usually a manual activity, which requires a large amount of work to draw the processes and their links. The current research works aim at building such collaborative process cartography in an automated manner. Our principles are (i) to gather the essential and minimum initial collaborative knowledge (e.g. partners, shared functions and collaborative objectives) in models, ii) to deduce the missing knowledge with the help of a collaborative metamodel, an associated ontology and transformation rules and iii) to structure the deduced knowledge in a collaborative process cartography thanks to dedicated algorithms

Designing, Aligning, and Visualizing Service Systems

Service is a concept that separates the concerns of an organization into (1) the value created for users and (2) the way the organization manages its resources to provide this value. The discipline of management of information technology (IT) uses services to coordinate and to optimize the use of IT resources (servers, applications, databases, etc.) in a way that brings value to users. The concrete application of the service concept is challenging due to its abstract, interdependent and recursive nature. We experienced this challenge while collaborating with the IT department of our university (École Polytechnique Fédérale de Lausanne, EPFL) when the IT department adopted the IT Infrastructure Library (ITIL) best-practices framework for IT service management. As researchers, we have the goal of improving the understanding of services as a means to structuring what people and organizations do. In the context of the IT department, we studied how to apply the service concept internally within the IT department, and externally (as business services) in the overall organization. In this thesis, we model services by using systems thinking principles. In particular, we use and improve SEAM, the systemic service-modeling method developed in our laboratory. Our main result is an ontology for SEAM service modeling. Our contributions are the heuristics that define how the ontology relates to a perceived reality: for example, the heuristics focus on behavior rather than organization and they put an emphasis on service instances rather than service types. We also define alignment between service systems, based on the properties of the systems¿ behavior. We show how to model an organization by implementing the concept of service as defined by our ontology. This ontology supports the design of service systems that align across both IT and business services. During our work with over one hundred IT services, we developed several visualization prototypes of a service cartography; we use these prototypes to describe and to relate the different views required for managing services. Our results offer a concrete way to implement the abstract concept of services. This way could be of interest for any organization willing to embark on a large-scale service project

Business and logic charateristic in an collaborative situation

MISE 2.0 (for Mediation Information System Engineering, second iteration) project has been launched in 2009. The MISE 2.0 engineering approach is based on BPM (Business Process Management) and MDE (Model-Driven Engineering). Running a regular BPM approach on a specific organization consists in gathering structural, informational, and functional knowledge in order to design cartography of processes covering the behavior of the modeled organization. Regarding the MISE 2.0 project the BPM approach concerns a set of organizations and MDE helps in automatizing the different steps: i) Knowledge gathering (situation layer): collect information concerning the collaborative situation, ii) Processes cartography design (solution layer): design the processes according to the knowledge gathered and iii) MIS deployment (implementation layer): implement an IT structure able to run the processes cartography. Both the transitions between these layers are the hard-points of this approach: The first gap is managed at the abstract level of MISE 2.0 while the second one is managed at the concrete level of MISE 2.0. The current PhD is focused on the first issue: designing a relevant processes cartography from the modeled collaborative situation. However, this is usually a manual activity, which requires a large amount of work to draw the processes and their links. The current research works aim at building such collaborative process cartography in an automated manner. Our principles are (i) to gather the essential and minimum initial collaborative knowledge (e.g. partners, shared functions and collaborative objectives) in models, ii) to deduce the missing knowledge with the help of a collaborative metamodel, an associated ontology and transformation rules and iii) to structure the deduced knowledge in a collaborative process cartography thanks to dedicated algorithms

Intelligent Systems

This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier

Towards using intelligent techniques to assist software specialists in their tasks

L’automatisation et l’intelligence constituent des préoccupations majeures dans le domaine de l’Informatique. Avec l’évolution accrue de l’Intelligence Artificielle, les chercheurs et l’industrie se sont orientés vers l’utilisation des modèles d’apprentissage automatique et d’apprentissage profond pour optimiser les tâches, automatiser les pipelines et construire des systèmes intelligents. Les grandes capacités de l’Intelligence Artificielle ont rendu possible d’imiter et même surpasser l’intelligence humaine dans certains cas aussi bien que d’automatiser les tâches manuelles tout en augmentant la précision, la qualité et l’efficacité. En fait, l’accomplissement de tâches informatiques nécessite des connaissances, une expertise et des compétences bien spécifiques au domaine. Grâce aux puissantes capacités de l’intelligence artificielle, nous pouvons déduire ces connaissances en utilisant des techniques d’apprentissage automatique et profond appliquées à des données historiques représentant des expériences antérieures. Ceci permettra, éventuellement, d’alléger le fardeau des spécialistes logiciel et de débrider toute la puissance de l’intelligence humaine. Par conséquent, libérer les spécialistes de la corvée et des tâches ordinaires leurs permettra, certainement, de consacrer plus du temps à des activités plus précieuses. En particulier, l’Ingénierie dirigée par les modèles est un sous-domaine de l’informatique qui vise à élever le niveau d’abstraction des langages, d’automatiser la production des applications et de se concentrer davantage sur les spécificités du domaine. Ceci permet de déplacer l’effort mis sur l’implémentation vers un niveau plus élevé axé sur la conception, la prise de décision. Ainsi, ceci permet d’augmenter la qualité, l’efficacité et productivité de la création des applications. La conception des métamodèles est une tâche primordiale dans l’ingénierie dirigée par les modèles. Par conséquent, il est important de maintenir une bonne qualité des métamodèles étant donné qu’ils constituent un artéfact primaire et fondamental. Les mauvais choix de conception, ainsi que les changements conceptuels répétitifs dus à l’évolution permanente des exigences, pourraient dégrader la qualité du métamodèle. En effet, l’accumulation de mauvais choix de conception et la dégradation de la qualité pourraient entraîner des résultats négatifs sur le long terme. Ainsi, la restructuration des métamodèles est une tâche importante qui vise à améliorer et à maintenir une bonne qualité des métamodèles en termes de maintenabilité, réutilisabilité et extensibilité, etc. De plus, la tâche de restructuration des métamodèles est délicate et compliquée, notamment, lorsqu’il s’agit de grands modèles. De là, automatiser ou encore assister les architectes dans cette tâche est très bénéfique et avantageux. Par conséquent, les architectes de métamodèles pourraient se concentrer sur des tâches plus précieuses qui nécessitent de la créativité, de l’intuition et de l’intelligence humaine. Dans ce mémoire, nous proposons une cartographie des tâches qui pourraient être automatisées ou bien améliorées moyennant des techniques d’intelligence artificielle. Ensuite, nous sélectionnons la tâche de métamodélisation et nous essayons d’automatiser le processus de refactoring des métamodèles. A cet égard, nous proposons deux approches différentes: une première approche qui consiste à utiliser un algorithme génétique pour optimiser des critères de qualité et recommander des solutions de refactoring, et une seconde approche qui consiste à définir une spécification d’un métamodèle en entrée, encoder les attributs de qualité et l’absence des design smells comme un ensemble de contraintes et les satisfaire en utilisant Alloy.Automation and intelligence constitute a major preoccupation in the field of software engineering. With the great evolution of Artificial Intelligence, researchers and industry were steered to the use of Machine Learning and Deep Learning models to optimize tasks, automate pipelines, and build intelligent systems. The big capabilities of Artificial Intelligence make it possible to imitate and even outperform human intelligence in some cases as well as to automate manual tasks while rising accuracy, quality, and efficiency. In fact, accomplishing software-related tasks requires specific knowledge and skills. Thanks to the powerful capabilities of Artificial Intelligence, we could infer that expertise from historical experience using machine learning techniques. This would alleviate the burden on software specialists and allow them to focus on valuable tasks. In particular, Model-Driven Engineering is an evolving field that aims to raise the abstraction level of languages and to focus more on domain specificities. This allows shifting the effort put on the implementation and low-level programming to a higher point of view focused on design, architecture, and decision making. Thereby, this will increase the efficiency and productivity of creating applications. For its part, the design of metamodels is a substantial task in Model-Driven Engineering. Accordingly, it is important to maintain a high-level quality of metamodels because they constitute a primary and fundamental artifact. However, the bad design choices as well as the repetitive design modifications, due to the evolution of requirements, could deteriorate the quality of the metamodel. The accumulation of bad design choices and quality degradation could imply negative outcomes in the long term. Thus, refactoring metamodels is a very important task. It aims to improve and maintain good quality characteristics of metamodels such as maintainability, reusability, extendibility, etc. Moreover, the refactoring task of metamodels is complex, especially, when dealing with large designs. Therefore, automating and assisting architects in this task is advantageous since they could focus on more valuable tasks that require human intuition. In this thesis, we propose a cartography of the potential tasks that we could either automate or improve using Artificial Intelligence techniques. Then, we select the metamodeling task and we tackle the problem of metamodel refactoring. We suggest two different approaches: A first approach that consists of using a genetic algorithm to optimize set quality attributes and recommend candidate metamodel refactoring solutions. A second approach based on mathematical logic that consists of defining the specification of an input metamodel, encoding the quality attributes and the absence of smells as a set of constraints and finally satisfying these constraints using Alloy

Development and Assessment of a Spatial Decision Support System for Conservation Planning

Land conservation is frequently cited as the most effective means of limiting the detrimental effects of anthropogenic forces on natural resources. Because governmental entities can be hampered by fiscal and political concerns, land trusts are increasing relied on to protect habitat. However, these groups often lack the analysis and research tools necessary to meet their mission. Geographic Information System (GIs) technologies such as Spatial Decision Support Systems (SDSS) offer the promise of allowing decision makers to explore their decision space at a landscape level of analysis. But critics have charged that research in this arena is largely anecdotal in nature. This research explores the validity of this contention and presents two applied empirical studies of user satisfaction with an SDSS. In order to assess the overall maturity of the GIs discipline, articles in four journals from 1996 to 2001 were analyzed based on the scientific rigor of the research strategies employed. The results showed that, while there was an increase in the breadth of methodologies employed, the majority of studies employed qualitative ( hypothesis generating ) rather than empirical ( hypothesis testing ) designs. The findings showed need for scientifically rigorous studies in applied settings. An operational SDSS was designed that identified and prioritized suitable land parcels for protection given multiple criteria and user values. The SDSS was customized for a single land trust in Maine and four theories of user acceptance of technology were tested using a modification of the traditional case study methodology. The Relative Advantage theory provided the best explanation for user acceptance of the technology. The research design also overcame the hurdles to conducting case study research in an empirical manner. In the next stage of research, the SDSS was distributed to eighty-one land trusts for testing. An analysis of the twenty-four returned surveys indicated strong support for the User Competence theory. To the author\u27s knowledge, these two studies represented the first experimental SDSS research in an applied rather than laboratory setting