Architecting the Future: A Model for Enterprise Integration in the Metaverse

Although it has a history that goes back about three decades, Metaverse has grown to be one of the most talked-about subjects today. Metaverse gradually increased its influence in the realm of business discourse after initially being restricted to discussions about entertainment. Before getting deep into the Metaverse, it should be noted that failure and deviating from the business path are highly likely for an enterprise that relies heavily on information technology (IT) because of improper use and thinking about IT. The idea of enterprise architecture (EA) emerged as a management strategy to address this issue. As the first school of thought of EA, it sought to transform IT from an unnecessary burden in an enterprise to a guiding and supporting force. Then an extended EA model is suggested as a result of the attempt made in this paper to use the idea of EA to steer virtual enterprises on Metaverse-based platforms. Finally, to evaluate the conceptual model and demonstrate that the Metaverse can support businesses, three case studies Decentraland, Battle Infinity, and Rooom were utilized.Comment: 10 pages, 6 figures, 3 table

What we know and what we do not know about DMN

The recent Decision Model and Notation (DMN) establishes business decisions as first-class citizens of executable business processes. This research note has two objectives: first, to describe DMN's technical and theoretical foundations; second, to identify research directions for investigating DMN's potential benefits on a technological, individual and organizational level. To this end, we integrate perspectives from management science, cognitive theory and information systems research

Digital Transformation Models for the I4.0 Transition: Lessons from the Change Management Literature

The growing diffusion of digital technologies, especially in production systems, is leading to a new industrial paradigm, named Industry 4.0 (I4.0), which involves disruptive changes in the way companies organize production and create value. Organizations willing to seize the opportunities of I4.0 must thus innovate their processes and business models. The challenges that companies must face for the transition towards I4.0 paradigm are not trivial. Several digital transformation models and roadmaps have been lately proposed in the literature to support companies in such a transition. The literature on change management stresses that about 70% of change initiatives—independently of the aim—fail to achieve their goals due to the implementation of transformation programs that are affected by well-known mistakes or neglect some relevant aspects, such as lack of management support, lack of clearly defined and achievable objectives and poor communication. This paper investigates whether and to what extent the existing digital transformation models (DTMs) and roadmaps for I4.0 transition consider the lessons learnt in the field of change management. To this aim, a Systematic Literature Review to identify existing models and roadmaps is carried out. The results obtained by the review are discussed under the lens of the change-management literature. Based on that, the shortcomings and weaknesses of existing DTMs are pinpointed. Extant DTMs mainly focus on digital transformation initiatives carried out in manufacturing companies; they do not cover all the phases of the digital transformation process but rather focus on the definition of the I4.0 vision, strategy and roadmap. Little attention is devoted to the implementation and consolidation of digital change. Change management lessons are considered to a limited extent, based on which, some suggestions for better dealing with digital transformation initiatives are discussed. The paper contributes to advancing knowledge on models and approaches to support organizations in managing digital transformation. The identification of change management activities that a digital transformation initiative should involve as well as the suggestions on how to effectively deal with it can be used by managers to successfully lead the I4.0 transition journey in their organizations

Interim research assessment 2003-2005 - Computer Science

This report primarily serves as a source of information for the 2007 Interim Research Assessment Committee for Computer Science at the three technical universities in the Netherlands. The report also provides information for others interested in our research activities

Software Test Case Generation Tools and Techniques: A Review

Software Industry is evolving at a very fast pace since last two decades. Many software developments, testing and test case generation approaches have evolved in last two decades to deliver quality products and services. Testing plays a vital role to ensure the quality and reliability of software products. In this paper authors attempted to conduct a systematic study of testing tools and techniques. Six most popular e-resources called IEEE, Springer, Association for Computing Machinery (ACM), Elsevier, Wiley and Google Scholar to download 738 manuscripts out of which 125 were selected to conduct the study. Out of 125 manuscripts selected, a good number approx. 79% are from reputed journals and around 21% are from good conference of repute. Testing tools discussed in this paper have broadly been divided into five different categories: open source, academic and research, commercial, academic and open source, and commercial & open source. The paper also discusses several benchmarked datasets viz. Evosuite 10, SF100 Corpus, Defects4J repository, Neo4j, JSON, Mocha JS, and Node JS to name a few. Aim of this paper is to make the researchers aware of the various test case generation tools and techniques introduced in the last 11 years with their salient features

Détection des écarts de tendance et analyse prédictive pour le traitement des flux d’événements en temps réel

Les systèmes d’information produisent différents types de journaux d’événements. Les données historiques contenues dans les journaux d’événements peuvent révéler des informations importantes sur l’exécution d’un processus métier. Le volume croissant de ces données collectées, pour être utile, doit être traité afin d’extraire des informations pertinentes. Dans de nombreuses situations, il peut être souhaitable de rechercher des tendances dans ces journaux. En particulier, les tendances calculées par le traitement et l’analyse de la séquence d’événements générés par plusieurs instances du même processus servent de base pour produire des prévisions sur les exécutions actuelles du processus. L’objectif de cette thèse est de proposer un cadre générique pour l’analyse des tendances sur ces flux d’événement, en temps réel. En premier lieu, nous montrons comment des tendances de différents types peuvent être calculées sur des journaux d’événements en temps réel, à l’aide d’un cadre générique appelé workflow de distance de tendance. De multiples calculs courants sur les flux d’événements s’avèrent être des cas particuliers de ce flux de travail, selon la façon dont différents paramètres de flux de travail sont définis. La suite naturelle de l’analyse statique des tendances est l’usage des algorithmes d’apprentissage. Nous joignons alors les concepts de traitement de flux d’événements et d’apprentissage automatique pour créer un cadre qui permet le calcul de différents types de prédictions sur les journaux d’événements. Le cadre proposé est générique : en fournissant différentes définitions à une poignée de fonctions d’événement, plusieurs types de prédictions différents peuvent être calculés à l’aide du même flux de travail de base. Les deux approches ont été mises en oeuvre et évaluées expérimentalement en étendant un moteur de traitement de flux d’événements existant, appelé BeepBeep. Les résultats expérimentaux montrent que les écarts par rapport à une tendance de référence peuvent être détectés en temps réel pour des flux produisant jusqu’à des milliers d’événements par seconde

Educational Technology and Related Education Conferences for June to December 2015

The 33rd edition of the conference list covers selected events that primarily focus on the use of technology in educational settings and on teaching, learning, and educational administration. Only listings until December 2015 are complete as dates, locations, or Internet addresses (URLs) were not available for a number of events held from January 2016 onward. In order to protect the privacy of individuals, only URLs are used in the listing as this enables readers of the list to obtain event information without submitting their e-mail addresses to anyone. A significant challenge during the assembly of this list is incomplete or conflicting information on websites and the lack of a link between conference websites from one year to the next