Digital and Strategic Innovation for Alpine Health Tourism

This open access book presents a set of practical tools and collaborative solutions in multi-disciplinary settings to foster the Alpine Space health tourism industry’s innovation and competitiveness. The proposed solutions emerge as the result of the synergy among health, environment, tourism, digital, policy and strategy professionals. The approach underlines the pivotal role of a sustainable and ecomedical use of Alpine natural resources for health tourism destinations, and highlights the need of integrating aspects of natural resources’ healing effects, a shared knowledge of Alpine assets through digital solutions, and frames strategic approaches for the long-term development of the sector. The volume exploits the results of the three-years long EU research project HEALPS 2, which involved several stakeholders from the health tourism, healthcare and sustainable tourism industries. This book is relevant for health tourism destinations and facilities (hotels, clinics, wellness and spa companies), regional and local authorities (policy makers), business support organizations, researchers involved in digital healthcare and geoinformatics

Digital and Strategic Innovation for Alpine Health Tourism

This open access book presents a set of practical tools and collaborative solutions in multi-disciplinary settings to foster the Alpine Space health tourism industry’s innovation and competitiveness. The proposed solutions emerge as the result of the synergy among health, environment, tourism, digital, policy and strategy professionals. The approach underlines the pivotal role of a sustainable and ecomedical use of Alpine natural resources for health tourism destinations, and highlights the need of integrating aspects of natural resources’ healing effects, a shared knowledge of Alpine assets through digital solutions, and frames strategic approaches for the long-term development of the sector. The volume exploits the results of the three-years long EU research project HEALPS 2, which involved several stakeholders from the health tourism, healthcare and sustainable tourism industries. This book is relevant for health tourism destinations and facilities (hotels, clinics, wellness and spa companies), regional and local authorities (policy makers), business support organizations, researchers involved in digital healthcare and geoinformatics

Digital and Strategic Innovation for Alpine Health Tourism

This open access book presents a set of practical tools and collaborative solutions in multi-disciplinary settings to foster the Alpine Space health tourism industry’s innovation and competitiveness. The proposed solutions emerge as the result of the synergy among health, environment, tourism, digital, policy and strategy professionals. The approach underlines the pivotal role of a sustainable and ecomedical use of Alpine natural resources for health tourism destinations, and highlights the need of integrating aspects of natural resources’ healing effects, a shared knowledge of Alpine assets through digital solutions, and frames strategic approaches for the long-term development of the sector. The volume exploits the results of the three-years long EU research project HEALPS 2, which involved several stakeholders from the health tourism, healthcare and sustainable tourism industries. This book is relevant for health tourism destinations and facilities (hotels, clinics, wellness and spa companies), regional and local authorities (policy makers), business support organizations, researchers involved in digital healthcare and geoinformatics

Geoscience data publication: practices and perspectives on enabling the FAIR guiding principles

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Kinkade, D., & Shepherd, A. Geoscience data publication: practices and perspectives on enabling the FAIR guiding principles. Geoscience Data Journal, (2021): https://doi.org/10.1002/gdj3.120.ntroduced in 2016, the FAIR Guiding Principles endeavour to significantly improve the process of today's data-driven research. The Principles present a concise set of fundamental concepts that can facilitate the findability, accessibility, interoperability and reuse (FAIR) of digital research objects by both machines and human beings. The emergence of FAIR has initiated a flurry of activity within the broader data publication community, yet the principles are still not fully understood by many community stakeholders. This has led to challenges such as misinterpretation and co-opted use, along with persistent gaps in current data publication culture, practices and infrastructure that need to be addressed to achieve a FAIR data end-state. This paper presents an overview of the practices and perspectives related to the FAIR Principles within the Geosciences and offers discussion on the value of the principles in the larger context of what they are trying to achieve. The authors of this article recommend using the principles as a tool to bring awareness to the types of actions that can improve the practice of data publication to meet the needs of all data consumers. FAIR Guiding Principles should be interpreted as an aspirational guide to focus behaviours that lead towards a more FAIR data environment. The intentional discussions and incremental changes that bring us closer to these aspirations provide the best value to our community as we build the capacity that will support and facilitate new discovery of earth systems.The writing of this article was supported by the NSF, grant no. 1924618

Improving knowledge about the risks of inappropriate uses of geospatial data by introducing a collaborative approach in the design of geospatial databases

La disponibilité accrue de l’information géospatiale est, de nos jours, une réalité que plusieurs organisations, et même le grand public, tentent de rentabiliser; la possibilité de réutilisation des jeux de données est désormais une alternative envisageable par les organisations compte tenu des économies de coûts qui en résulteraient. La qualité de données de ces jeux de données peut être variable et discutable selon le contexte d’utilisation. L’enjeu d’inadéquation à l’utilisation de ces données devient d’autant plus important lorsqu’il y a disparité entre les nombreuses expertises des utilisateurs finaux de la donnée géospatiale. La gestion des risques d’usages inappropriés de l’information géospatiale a fait l’objet de plusieurs recherches au cours des quinze dernières années. Dans ce contexte, plusieurs approches ont été proposées pour traiter ces risques : parmi ces approches, certaines sont préventives et d’autres sont plutôt palliatives et gèrent le risque après l'occurrence de ses conséquences; néanmoins, ces approches sont souvent basées sur des initiatives ad-hoc non systémiques. Ainsi, pendant le processus de conception de la base de données géospatiale, l’analyse de risque n’est pas toujours effectuée conformément aux principes d’ingénierie des exigences (Requirements Engineering) ni aux orientations et recommandations des normes et standards ISO. Dans cette thèse, nous émettons l'hypothèse qu’il est possible de définir une nouvelle approche préventive pour l’identification et l’analyse des risques liés à des usages inappropriés de la donnée géospatiale. Nous pensons que l’expertise et la connaissance détenues par les experts (i.e. experts en geoTI), ainsi que par les utilisateurs professionnels de la donnée géospatiale dans le cadre institutionnel de leurs fonctions (i.e. experts du domaine d'application), constituent un élément clé dans l’évaluation des risques liés aux usages inadéquats de ladite donnée, d’où l’importance d’enrichir cette connaissance. Ainsi, nous passons en revue le processus de conception des bases de données géospatiales et proposons une approche collaborative d’analyse des exigences axée sur l’utilisateur. Dans le cadre de cette approche, l’utilisateur expert et professionnel est impliqué dans un processus collaboratif favorisant l’identification a priori des cas d’usages inappropriés. Ensuite, en passant en revue la recherche en analyse de risques, nous proposons une intégration systémique du processus d’analyse de risque au processus de la conception de bases de données géospatiales et ce, via la technique Delphi. Finalement, toujours dans le cadre d’une approche collaborative, un référentiel ontologique de risque est proposé pour enrichir les connaissances sur les risques et pour diffuser cette connaissance aux concepteurs et utilisateurs finaux. L’approche est implantée sous une plateforme web pour mettre en œuvre les concepts et montrer sa faisabilité.Nowadays, the increased availability of geospatial information is a reality that many organizations, and even the general public, are trying to transform to a financial benefit. The reusability of datasets is now a viable alternative that may help organizations to achieve cost savings. The quality of these datasets may vary depending on the usage context. The issue of geospatial data misuse becomes even more important because of the disparity between the different expertises of the geospatial data end-users. Managing the risks of geospatial data misuse has been the subject of several studies over the past fifteen years. In this context, several approaches have been proposed to address these risks, namely preventive approaches and palliative approaches. However, these approaches are often based on ad-hoc initiatives. Thus, during the design process of the geospatial database, risk analysis is not always carried out in accordance neither with the principles/guidelines of requirements engineering nor with the recommendations of ISO standards. In this thesis, we suppose that it is possible to define a preventive approach for the identification and analysis of risks associated to inappropriate use of geospatial data. We believe that the expertise and knowledge held by experts and users of geospatial data are key elements for the assessment of risks of geospatial data misuse of this data. Hence, it becomes important to enrich that knowledge. Thus, we review the geospatial data design process and propose a collaborative and user-centric approach for requirements analysis. Under this approach, the user is involved in a collaborative process that helps provide an a priori identification of inappropriate use of the underlying data. Then, by reviewing research in the domain of risk analysis, we propose to systematically integrate risk analysis – using the Delphi technique – through the design of geospatial databases. Finally, still in the context of a collaborative approach, an ontological risk repository is proposed to enrich the knowledge about the risks of data misuse and to disseminate this knowledge to the design team, developers and end-users. The approach is then implemented using a web platform in order to demonstrate its feasibility and to get the concepts working within a concrete prototype

From Expert Discipline to Common Practice: A Vision and Research Agenda for Extending the Reach of Enterprise Modeling

The benefits of enterprise modeling (EM) and its contribution to organizational tasks are largely undisputed in business and information systems engineering. EM as a discipline has been around for several decades but is typically performed by a limited number of people in organizations with an affinity to modeling. What is captured in models is only a fragment of what ought to be captured. Thus, this research note argues that EM is far from its maximum potential. Many people develop some kind of model in their local practice without thinking about it consciously. Exploiting the potential of this “grass roots modeling” could lead to groundbreaking innovations. The aim is to investigate integration of the established practices of modeling with local practices of creating and using model-like artifacts of relevance for the overall organization. The paper develops a vision for extending the reach of EM, identifies research areas contributing to the vision and proposes elements of a future research Agenda

Framework for collaborative knowledge management in organizations

Nowadays organizations have been pushed to speed up the rate of industrial transformation to high value products and services. The capability to agilely respond to new market demands became a strategic pillar for innovation, and knowledge management could support organizations to achieve that goal. However, current knowledge management approaches tend to be over complex or too academic, with interfaces difficult to manage, even more if cooperative handling is required. Nevertheless, in an ideal framework, both tacit and explicit knowledge management should be addressed to achieve knowledge handling with precise and semantically meaningful definitions. Moreover, with the increase of Internet usage, the amount of available information explodes. It leads to the observed progress in the creation of mechanisms to retrieve useful knowledge from the huge existent amount of information sources. However, a same knowledge representation of a thing could mean differently to different people and applications. Contributing towards this direction, this thesis proposes a framework capable of gathering the knowledge held by domain experts and domain sources through a knowledge management system and transform it into explicit ontologies. This enables to build tools with advanced reasoning capacities with the aim to support enterprises decision-making processes. The author also intends to address the problem of knowledge transference within an among organizations. This will be done through a module (part of the proposed framework) for domain’s lexicon establishment which purpose is to represent and unify the understanding of the domain’s used semantic

Towards An Analysis Driven Approach for Adapting Enterprise Architecture Languages

Abstract: Enterprise Architecture (EA) modeling languages are increasingly used for various enterprise wide analyses. In most cases one needs to adapt EA languages to an appropriate level of detail. However such an adaptation is not straightforward. Language engineers currently deal with analysis driven language adaptation in an ad-hoc manner, adapting languages from scratch. This introduces various problems, such as a tendency to add uninteresting and/or unnecessary details to languages, while important enterprise details are not documented. Moreover, adding detail increases the complexity of languages, which in turn inhibits a language's communication capabilities. Yet experience from practice shows that architects often are communicators, next to analysts. As a result, one needs to find a balance between a model's communication and analysis capabilities. In this position paper we argue for an approach for assisting language engineers in adapting, in a controlled manner, EA languages for model-driven enterprise analyses. Furthermore, we present the key ingredients of such an approach, and use these as a starting point for a research outlook

-ilities Tradespace and Affordability Project – Phase 3

One of the key elements of the SERC’s research strategy is transforming the practice of systems engineering and associated management practices – “SE and Management Transformation (SEMT).” The Grand Challenge goal for SEMT is to transform the DoD community’s current systems engineering and management methods, processes, and tools (MPTs) and practices away from sequential, single stovepipe system, hardware-first, document-driven, point- solution, acquisition-oriented approaches; and toward concurrent, portfolio and enterprise- oriented, hardware-software-human engineered, model-driven, set-based, full life cycle approaches.This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) under Contract H98230-08- D-0171 (Task Order 0031, RT 046).This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) under Contract H98230-08- D-0171 (Task Order 0031, RT 046)

Active Learning for Reducing Labeling Effort in Text Classification Tasks

Labeling data can be an expensive task as it is usually performed manually by domain experts. This is cumbersome for deep learning, as it is dependent on large labeled datasets. Active learning (AL) is a paradigm that aims to reduce labeling effort by only using the data which the used model deems most informative. Little research has been done on AL in a text classification setting and next to none has involved the more recent, state-of-the-art Natural Language Processing (NLP) models. Here, we present an empirical study that compares different uncertainty-based algorithms with BERT$_{base}$ as the used classifier. We evaluate the algorithms on two NLP classification datasets: Stanford Sentiment Treebank and KvK-Frontpages. Additionally, we explore heuristics that aim to solve presupposed problems of uncertainty-based AL; namely, that it is unscalable and that it is prone to selecting outliers. Furthermore, we explore the influence of the query-pool size on the performance of AL. Whereas it was found that the proposed heuristics for AL did not improve performance of AL; our results show that using uncertainty-based AL with BERT$_{base}$ outperforms random sampling of data. This difference in performance can decrease as the query-pool size gets larger.Comment: Accepted as a conference paper at the joint 33rd Benelux Conference on Artificial Intelligence and the 30th Belgian Dutch Conference on Machine Learning (BNAIC/BENELEARN 2021). This camera-ready version submitted to BNAIC/BENELEARN, adds several improvements including a more thorough discussion of related work plus an extended discussion section. 28 pages including references and appendice