19th IEEE International Conference on Computational Science and Engineering (CSE 2016), is the event, in a series of highly successful International Conferences on Computational Science and Engineering, held mainly as the International Workshop on High Performance Scientific and Engineering Computing for 11 editions. August 24-26, 2016 - Paris, France / Currently multimedia contents dominate the information exchanged in Internet, particularly through social networks. Each actor on the Internet becomes producer and consumer of contents. Nevertheless, social network and other traditional collaborative environments present limitations regarding content selection, categorization, aggregation, linking and interoperability, and usage control and privacy. In [1], we proposed the architecture (based on a peer-to-peer infrastructure and Semantic Web) of a MultiMedia Digital EcoSystem (MMDES), as a new environment for collaboration and sharing of multimedia resources, multimedia processings, as well as for computing and storage capabilities. In this paper, we describe MMDES framework and functionalities related to managing the collective knowledge and equilibrium in MMDES. We also describe the implementation of MMDES using a mobile platform in order to provide resources’ sharing for the Archivo Nacional de Arte Rupestre (ANAR) in Venezuela

A rápida evolução e a ampla disseminação de tecnologias digitais estão mudando o contexto e o perfil dos aprendizes que hoje circulam naturalmente entre espaços físicos, espaços virtuais e espaços sociais diferentes, aprendendo em contextos formais e informais. Paradigmas educacionais atuais defendem a aprendizagem centrada no aprendiz que constrói seu conhecimento ao criar e desenvolver projetos, ao interagir com os objetos de estudo, com seus pares, com seus professores e mentores. A análise de requisitos etapa fundamental do desenvolvimento de uma ferramenta digital - concentra-se essencialmente em aspectos computacionais a partir da identificação de fluxos de entrada e saída e dos processos que a ferramenta deverá executar. Entretanto, a análise de requisitos é difícil de ser conduzida quando os conteúdos e inter-relações são complexos e dinâmicos. Recentemente, a abordagem de ecossistemas tem sido usada para entender ou modelar fenômenos que surgem da tecnologia e de seu uso. Propomos utilizar esta abordagem para conceber novas ferramentas digitais de aprendizagem ao analisar seus requisitos, ou para analisar ferramentas existentes. Assim, propomos neste trabalho uma definição e um modelo de Ecossistema Digital de Aprendizagem, que podem ser aplicados tanto na concepção de novas ferramentas educacionais quanto na análise para melhoria de ferramentas existentes. Um conjunto de artefatos, resultantes do detalhamento do modelo, é apresentado a fim de auxiliar na utilização do mesmo. A avaliação da aplicabilidade do modelo foi realizada por meio de sua utilização em estudos de caso de ferramentas educacionais desenvolvidas anteriormente. A aplicação do modelo evidenciou aspectos que não foram contemplados com abordagens tradicionais e permitiu levantar possíveis modificações e ampliações que podem levar a um estágio mais maduro do ecossistema. Os resultados obtidos com a aplicação do modelo na análise de ferramentas existentes apontam caminhos promissores para que esta estratégia seja usada na concepção de novas ferramentas educacionais de aprendizagem. / The rapid evolution and dissemination of digital technology are changing the learners context and profile. Learners move naturally between different physical spaces, different virtual spaces and different social spaces, engaging in learning activities in formal and informal contexts. Educational paradigms defend a learner centered approach, where learners construct their knowledge creating and developing projects, interacting with learning objects, with their peers, their teachers and mentors. Requirements analysis is a fundamental phase in the development of digital tools and concentrates essentially on computational aspects consisting on the identification of the tools input and output flows as well as processing. Nevertheless, requirements analysis is difficult to conduct when contents and relations are complex and dynamic. Recently, the ecosystem approach has been used to understand and to model phenomena that appear from the technology and its use. We propose to use this approach to conceive new digital learning tools, analyzing its requirements, or to use this approach to analyze existing tools. Therefore we propose in this research a Digital Learning Ecosystem definition and model that can be applied to the conception of new educational tools, as well as to analyze and improve existing tools. The detailed model includes a set of artifacts that can support its application. The model applicability evaluation was achieved by applying it on previously developed study cases. The model application showed some aspects that were not considered with traditional approaches and raised possible modifications and extensions that can lead to a mature stage of the ecosystem. The model application analysis of existing tools showed promising results and indicate that this approach can be used to conceive new educational learning tools.

A rápida evolução e a ampla disseminação de tecnologias digitais estão mudando o contexto e o perfil dos aprendizes que hoje circulam naturalmente entre espaços físicos, espaços virtuais e espaços sociais diferentes, aprendendo em contextos formais e informais. Paradigmas educacionais atuais defendem a aprendizagem centrada no aprendiz que constrói seu conhecimento ao criar e desenvolver projetos, ao interagir com os objetos de estudo, com seus pares, com seus professores e mentores. A análise de requisitos etapa fundamental do desenvolvimento de uma ferramenta digital - concentra-se essencialmente em aspectos computacionais a partir da identificação de fluxos de entrada e saída e dos processos que a ferramenta deverá executar. Entretanto, a análise de requisitos é difícil de ser conduzida quando os conteúdos e inter-relações são complexos e dinâmicos. Recentemente, a abordagem de ecossistemas tem sido usada para entender ou modelar fenômenos que surgem da tecnologia e de seu uso. Propomos utilizar esta abordagem para conceber novas ferramentas digitais de aprendizagem ao analisar seus requisitos, ou para analisar ferramentas existentes. Assim, propomos neste trabalho uma definição e um modelo de Ecossistema Digital de Aprendizagem, que podem ser aplicados tanto na concepção de novas ferramentas educacionais quanto na análise para melhoria de ferramentas existentes. Um conjunto de artefatos, resultantes do detalhamento do modelo, é apresentado a fim de auxiliar na utilização do mesmo. A avaliação da aplicabilidade do modelo foi realizada por meio de sua utilização em estudos de caso de ferramentas educacionais desenvolvidas anteriormente. A aplicação do modelo evidenciou aspectos que não foram contemplados com abordagens tradicionais e permitiu levantar possíveis modificações e ampliações que podem levar a um estágio mais maduro do ecossistema. Os resultados obtidos com a aplicação do modelo na análise de ferramentas existentes apontam caminhos promissores para que esta estratégia seja usada na concepção de novas ferramentas educacionais de aprendizagem. / The rapid evolution and dissemination of digital technology are changing the learners context and profile. Learners move naturally between different physical spaces, different virtual spaces and different social spaces, engaging in learning activities in formal and informal contexts. Educational paradigms defend a learner centered approach, where learners construct their knowledge creating and developing projects, interacting with learning objects, with their peers, their teachers and mentors. Requirements analysis is a fundamental phase in the development of digital tools and concentrates essentially on computational aspects consisting on the identification of the tools input and output flows as well as processing. Nevertheless, requirements analysis is difficult to conduct when contents and relations are complex and dynamic. Recently, the ecosystem approach has been used to understand and to model phenomena that appear from the technology and its use. We propose to use this approach to conceive new digital learning tools, analyzing its requirements, or to use this approach to analyze existing tools. Therefore we propose in this research a Digital Learning Ecosystem definition and model that can be applied to the conception of new educational tools, as well as to analyze and improve existing tools. The detailed model includes a set of artifacts that can support its application. The model applicability evaluation was achieved by applying it on previously developed study cases. The model application showed some aspects that were not considered with traditional approaches and raised possible modifications and extensions that can lead to a mature stage of the ecosystem. The model application analysis of existing tools showed promising results and indicate that this approach can be used to conceive new educational learning tools.

As data availability increases so do the opportunities within businesses. Companies need to explore technologies that are able to exploit and capitalise on this vast amount of data in order to stay relevant in today’s competitive market. Artificial intelligence and simulation are two promising technologies that are able to manage and utilise these large amounts of data. This paper explores the opportunities and challenges that exist of combining artificial intelligence with simulation in order to achieve data-driven decisions within industries. Although these two technologies are well researched in isolation, their combination and synergetic effects remain largely unexplored. The aim of this study is to survey this existing vacuum by performing a literature review and producing a digitised AI and simulation ecosystem that encapsulates the opportunities and challenges enabled by these two technologies. This research explored this ecosystem by applying and developing it on a real case study of an automotive parts supplier’s production process. It was concluded that this modularised digitised ecosystem could act as an alternative to expensive and generic software solutions due to its high customisation, simple integration and cost-efficiency, especially for SMEs. The study also concluded that adding additional AI and simulation models to the ecosystem reduces the modules’ unit costs since they can share some high cost structures such as: databases, servers and user-interfaces; this idea was encapsulated in the term digitised economies of scale.

No / Rapid advances in software systems, wireless networks, and embedded devices have led to the development of a pervasive and mobile cyberspace that provides an infrastructure for anywhere/anytime service provisioning in different domains such as engineering, commerce, education, and entertainment. This style of service provisioning enables users to freely move between geographical areas and to continuously access information and conduct online transactions. However, such a high mobility may cause performance and reliability problems during the execution of transactions. For example, the unavailability of sufficient bandwidth can result in failure of transactions when users move from one area (cell) to another. We present a context-aware transaction model that dynamically adapts to the users' needs and execution environments. Accordingly, we develop a new mobility management scheme that ensures seamless connectivity and reliable execution of context-aware transactions during mobility of users. The proposed scheme is designed and developed using a combination of different queuing models. We conduct various experiments in order to show that the proposed scheme optimizes the mobility management process and increases the throughput of context-aware transactions.

O crescimento exponencial do uso das mídias sociais na Web está transformando a maneira de como as pessoas tratam as informações, interagem com elas e compartilham conhecimento. Da mesma forma, as organizações estão mudando a maneira de interagir com seus funcionários, parceiros e consumidores. Novas aplicações na Internet têm surgido para proporcionar confiança aos usuários e incentivá-los a interagir e conectá-los uns aos outros e a conteúdos disponibilizados. Essas aplicações podem identificar comportamentos, extrair opiniões e retornar informações de interesse dos usuários e das organizações de maneira a auxiliar a tomada de decisão. Essas aplicações proporcionam grande volume de dados e demandam complexos processos de análise. Essas análises abrem oportunidades para o desenvolvimento de novas soluções que agregam mais valor aos usuários de produtos e serviços disponibilizadas na Web. Empresas e instituições de pesquisa têm desenvolvidos meios para tratar o grande volume de dados e identificar oportunidades de negócio. O uso de modelos que permitem entender esse fenômeno coletivo tem aumentado nos últimos anos por, basicamente, duas razões: a necessidade de descobrir, organizar e representar o conhecimento empírico relacionado a um determinado domínio de interesse e a necessidade de disseminar mecanismos para auxiliar os tomadores de decisão. Nesse contexto, ontologias de domínio têm sido bastante utilizadas como forma de organização e representação do conhecimento. No entanto, são poucos os modelos ou aplicações que extraem, organizam e representam o conhecimento (implícito e explícito) contextualizado de grupos de pessoas que atuam coletivamente para resolver problemas comuns ou produzir algo novo. Este trabalho de pesquisa propõe um modelo de referência para extração da inteligência coletiva (IC) para suporte à tomada de decisão. O modelo foi inicialmente desenvolvido para caso do planejamento estratégico de TI para ser utilizada por órgãos de governo. Como parte do modelo, foi desenvolvida uma inovadora ontologia de domínio denominada ITMPvoc. De seu processo de construção e validação, o modelo extrai a IC que é contextualiza segundo o referencial 5W1H (What, Who, Why, Where, When, How) e aplicada para suporte à decisão em situações específicas. Outras instâncias do modelo para dois casos de uso são também apresentadas. São elas: extração da IC e suporte à decisão para alertas de doenças na agricultura e para alertas sobre adoção de software livre por municípios. Os resultados demonstram que os modelos de extração da IC de comunidades ou organizações humanas podem melhorar os complexos processos de tomada de decisão em colaboração. Verificou-se também que a melhoria do processo de tomada de decisão se dá de duas maneiras. A primeira pela compreensão mais ampla pela comunidade dos conceitos e seus relacionamentos de causa e efeito mapeados pelo referencial 5W1H. A segunda pela composição mais adequada dos componentes What, Who, Why, Where, When e How em função do contexto. Ambas maneiras contribuem para o enriquecimento do conhecimento sobre os domínios considerados. / The exponential growth in the use of social media on the Web is transforming the which people treat information, interact with it, and share knowledge. Similarly, organizations are changing the way they interact with their employees, partners, and consumers. New Internet applications have emerged to provide users with confidence and encourage them to interact and connect to each other and to access contents made available. These applications can identify behaviors, extract opinions, and return information of interest to users and organizations in order to support decision making. These applications provide large amounts of data and require complex analysis processes. These analyzes open opportunities for the development of new solutions that add value to users of products and services available on the Web. Companies and research institutions have developed means to handle the large volume of data and identify business opportunities. The use of models that allow to understand this collective phenomenon has increased in recent years for basically two reasons: the need to discover, organize and represent empirical knowledge related to a particular domain of interest and the need to disseminate mechanisms to support the decision-makers. In this context, domain ontologies have been widely used as a form of organization and representation of the knowledge. However, there are few models or applications that extract, organize, and represent the contextualized (implicit and explicit) knowledge of groups of people who act collectively to solve common problems or produce something new. This research proposes a reference model for extracting the collective intelligence (CI) for decision making support. The model was initially developed for strategic planning of IT to be used by government organizations. As part of the model, an innovative domain ontology called ITMPvoc was developed. From its construction and validation process, the model extracts the CI that is contextualized according to the 5W1H (What, Who, Why, Where, How) framework and it is applied for decision making support in specific situations. Other instances of the model are also presented for two use cases. They are: extraction and decision making support based on CI for (I) early warning disease in agriculture and (ii) early warning in adoption of free software by municipalities. The results demonstrate that the CI extraction model from human communities or organizations can improve complex collaborative decision-making processes. It was also found that the improvement of the decision-making process occurs in two ways. The first is by the community\'s broader understanding of concepts and their cause-and-effect relationships mapped by the 5W1H framework. The second is the most appropriate composition of the What, Who, Why, Where, When, and How components, according to the context. Both ways contribute to the enrichment of the knowledge about the considered domains.

O crescimento exponencial do uso das mídias sociais na Web está transformando a maneira de como as pessoas tratam as informações, interagem com elas e compartilham conhecimento. Da mesma forma, as organizações estão mudando a maneira de interagir com seus funcionários, parceiros e consumidores. Novas aplicações na Internet têm surgido para proporcionar confiança aos usuários e incentivá-los a interagir e conectá-los uns aos outros e a conteúdos disponibilizados. Essas aplicações podem identificar comportamentos, extrair opiniões e retornar informações de interesse dos usuários e das organizações de maneira a auxiliar a tomada de decisão. Essas aplicações proporcionam grande volume de dados e demandam complexos processos de análise. Essas análises abrem oportunidades para o desenvolvimento de novas soluções que agregam mais valor aos usuários de produtos e serviços disponibilizadas na Web. Empresas e instituições de pesquisa têm desenvolvidos meios para tratar o grande volume de dados e identificar oportunidades de negócio. O uso de modelos que permitem entender esse fenômeno coletivo tem aumentado nos últimos anos por, basicamente, duas razões: a necessidade de descobrir, organizar e representar o conhecimento empírico relacionado a um determinado domínio de interesse e a necessidade de disseminar mecanismos para auxiliar os tomadores de decisão. Nesse contexto, ontologias de domínio têm sido bastante utilizadas como forma de organização e representação do conhecimento. No entanto, são poucos os modelos ou aplicações que extraem, organizam e representam o conhecimento (implícito e explícito) contextualizado de grupos de pessoas que atuam coletivamente para resolver problemas comuns ou produzir algo novo. Este trabalho de pesquisa propõe um modelo de referência para extração da inteligência coletiva (IC) para suporte à tomada de decisão. O modelo foi inicialmente desenvolvido para caso do planejamento estratégico de TI para ser utilizada por órgãos de governo. Como parte do modelo, foi desenvolvida uma inovadora ontologia de domínio denominada ITMPvoc. De seu processo de construção e validação, o modelo extrai a IC que é contextualiza segundo o referencial 5W1H (What, Who, Why, Where, When, How) e aplicada para suporte à decisão em situações específicas. Outras instâncias do modelo para dois casos de uso são também apresentadas. São elas: extração da IC e suporte à decisão para alertas de doenças na agricultura e para alertas sobre adoção de software livre por municípios. Os resultados demonstram que os modelos de extração da IC de comunidades ou organizações humanas podem melhorar os complexos processos de tomada de decisão em colaboração. Verificou-se também que a melhoria do processo de tomada de decisão se dá de duas maneiras. A primeira pela compreensão mais ampla pela comunidade dos conceitos e seus relacionamentos de causa e efeito mapeados pelo referencial 5W1H. A segunda pela composição mais adequada dos componentes What, Who, Why, Where, When e How em função do contexto. Ambas maneiras contribuem para o enriquecimento do conhecimento sobre os domínios considerados. / The exponential growth in the use of social media on the Web is transforming the which people treat information, interact with it, and share knowledge. Similarly, organizations are changing the way they interact with their employees, partners, and consumers. New Internet applications have emerged to provide users with confidence and encourage them to interact and connect to each other and to access contents made available. These applications can identify behaviors, extract opinions, and return information of interest to users and organizations in order to support decision making. These applications provide large amounts of data and require complex analysis processes. These analyzes open opportunities for the development of new solutions that add value to users of products and services available on the Web. Companies and research institutions have developed means to handle the large volume of data and identify business opportunities. The use of models that allow to understand this collective phenomenon has increased in recent years for basically two reasons: the need to discover, organize and represent empirical knowledge related to a particular domain of interest and the need to disseminate mechanisms to support the decision-makers. In this context, domain ontologies have been widely used as a form of organization and representation of the knowledge. However, there are few models or applications that extract, organize, and represent the contextualized (implicit and explicit) knowledge of groups of people who act collectively to solve common problems or produce something new. This research proposes a reference model for extracting the collective intelligence (CI) for decision making support. The model was initially developed for strategic planning of IT to be used by government organizations. As part of the model, an innovative domain ontology called ITMPvoc was developed. From its construction and validation process, the model extracts the CI that is contextualized according to the 5W1H (What, Who, Why, Where, How) framework and it is applied for decision making support in specific situations. Other instances of the model are also presented for two use cases. They are: extraction and decision making support based on CI for (I) early warning disease in agriculture and (ii) early warning in adoption of free software by municipalities. The results demonstrate that the CI extraction model from human communities or organizations can improve complex collaborative decision-making processes. It was also found that the improvement of the decision-making process occurs in two ways. The first is by the community\'s broader understanding of concepts and their cause-and-effect relationships mapped by the 5W1H framework. The second is the most appropriate composition of the What, Who, Why, Where, When, and How components, according to the context. Both ways contribute to the enrichment of the knowledge about the considered domains.

The implementation of digital innovations in the healthcare sector is faced with different barriers and challenges. The complex system of regulations, the lack of interoperability, and highly dynamic interorganisational networks lead to missing widespread adoption of eHealth solutions. Digital platforms can help to overcome these barriers by providing a holistic infrastructure. They create a modularised foundation that innovators can use to create own innovations and provide them to demanders of digital solutions. As intermediaries, they can be accessed both by healthcare professionals and eHealth solution providers. Providers can offer their eHealth services via the platform. Healthcare professionals can use these services to create own interorganisational information systems. In the field of information systems research, effects and strategies for two-sided platforms are well researched and the potentials of eHealth platforms are also discussed. However, the organisational and technological design and methods for the construction of platforms are fewer questioned. Nonetheless, platform owners can benefit from implementation strategies and architectural guidance to create sustainable platforms and surrounding ecosystems. This doctoral thesis questions how domain-specific platforms can be designed systematically. Conducting a design-science research process, it develops both a modelling system and the Dresden Ecosystem Management Method (DREEM) to support the development of platforms in different domains. Furthermore, it describes the design characteristics of two-sided platforms in the healthcare sector and provides an evaluation approach to analyse the platforms’ ability to create a viable innovation ecosystem in the healthcare sector. The doctoral thesis contributes by providing methodical guidance for platform owners and researchers to design and evaluate digital platforms in different domains and improves the understanding of platform theory in the healthcare sector.:A. Synopsis of the Doctoral Thesis 1. Introduction 2. Foundational Considerations 3. Requirements for Design Artefacts and Knowledge 4. Structure of the Doctoral Thesis 5. Conclusion B. Paper 1 - Governance Guidelines for Digital Healthcare Ecosystems C. Paper 2 - Revise your eHealth Platform! D. Paper 3 - Business Model Open ”E-Health-Platform” E. Paper 4 - Modelling Ecosystems in Information Systems F. Paper 5 - Designing Industrial Symbiosis Platforms G. Paper 6 - Management of Digital Ecosystems with DREEM H. Paper 7 - Guiding the Development of Digital Ecosystems I. Paper 8 - Towards Maintenance Analytics Ecosystems J. Paper 9- Sustainability of E-Health-Projects K. Paper 10 - ISO 11354-2 for the Evaluation of eHealth-Platforms

Digital Health relates to “health information systems which enable the merging of social-care and healthcare systems. This would impact on the organisation, service delivery as well as the technological infrastructure” (Herselman & Botha, 2016, p.10). However, with relatively sparse research publications emanating from within the Namibian Health domain, and the concept of Namibian Digital Health as an emergent phenomenon, a Namibian Digital Health Innovation Ecosystem Framework would provide a start to conceptualising, developing and implementing such an ecosystem for Namibia and thus unlocking the potential of Digital Health in this country. The purpose of this study is to develop a Namibian Digital Health Innovation Ecosystem Framework based on literature reviews and the feedback from knowledgeable professionals (KPs) in Namibia, as well as global experts. The methodology which was applied in this study to address the purpose, and to answer the research questions, was Design Science Research Methodology and the Design Science Research Methodology (DSRM) process of Peffers, Tuunanen, Rothenberger and Chatterjee (2008), was adopted. Pragmatism is the overall philosophy guiding the study, as proposed by Ackoff’s theory regarding the hierarchy of human understanding (1989) and Shneiderman’s visual information seeking mantra (1996). During Phases 2 and 3 of the study interpretivism and positivism were applied as philosophies, guided by hermeneutics and triangulation, towards understanding the feedback of Knowledgeable Professionals (KPs) in Namibia, as well as the global experts. The study was divided into three phases. The first phase entailed a literature study which identified the components of Digital Health, Innovation and Digital Ecosystems as well as related research of Digital health, Innovation and Digital Ecosystems in developed and developing countries. This process led to the compilation of the initial Namibian Digital Health Innovation Ecosystem Framework using a conceptual approach. In the second phase of the study, the initial Namibian Digital Health Innovation Ecosystem was evaluated by KPs in Namibia using the Delphi method and interviews. Phase 2 adopted both quantitative and qualitative approaches. The findings from Phase 2 resulted in the development of the intermediate Namibian Digital Health Innovation Ecosystem Framework. In Phase 3 of the study, the intermediate framework was validated by global experts. Feedback was collected from global experts through questionnaires which were analysed through qualitative content analysis. The findings, from Phase 3 led to the development of the final Namibian Digital Health Innovation Ecosystems Framework. The guidelines, which can be used by the Namibian government to implement the suggested digital health innovation ecosystem framework, were also provided. / Information Science / D. Litt. et Phil. (Information Systems)

Följande examensarbete har genomförts inom Högskoleingenjörsutbildningen i Maskinteknik, Industriell Ekonomi och Produktion vid Kungliga Tekniska Högskolan, Institutionen för Hållbar Produktionsutveckling. Studien har utförts på uppdrag av Alfa Laval Lund AB, vid enheten Business Unit GPHE, under vårterminen 2019. Målet med detta arbete har varit att utföra en kartläggning av intressenter i det digitala ekosystemet, identifiera kundbehov och aktörer på marknaden inom området prediktivt underhåll och uppkopplade produkter samt att definiera möjliga affärsmodeller. Alfa Lavals förväntningar av projektet har varit att resultatet skall understödja uppfyllelse av kundernas behov samt att bolaget skall erhålla en överblick av det digitala ekosystem som idag växer fram. Under genomförandet har metoder såsom research av intressenter samt intervjuer varit centrala. Efterforskningar av bolag har skett med avsikten att utforma en kartläggningsmodell. Intervjuer med aktörer och slutkund har varit till stöd för att undersöka i vilka fall uppkoppling skapar kundvärde samt för att få en helhetssyn kring andra spelares strategier inom digitaliseringen. Litteraturstudier har omfattat information beträffande Internet of Things, Connectivity, Industri 4.0, prediktivt underhåll samt affärsmodellering. Resultatet har visat att de mest förekommande kundbehoven är statusmonitorering av utrustning, prediktivt underhåll och högre produktivitet med flera, vilka alla har anknytning till att kunden vill minska sina kostnader. För det digitala ekosystemet kan det konstateras att det aktuella läget är ganska splittrat samt att det ständigt dyker upp nya lösningar och spelare som försöker positionera sig på marknaden. Studien visar likaledes att uppkopplingen till internet bidrar till uppkomst av nya erbjudanden i form av tjänster, varför det förekommer ett ökat behov av att implementera nya serviceinriktade affärsmodeller med nya intäktsströmmar baserade på prenumerationer. / This thesis degree project has been submitted for the degree program in Mechanical Engineering, Industrial Business Administration and Manufacturing, at KTH Royal Institute of Technology, Department of Sustainable Production Development. The study has been carried out on behalf of Alfa Laval AB, Business Unit GPHE, during the spring term 2019. The goal of this thesis has been to perform a survey of interests in the digital ecosystem, identify customer needs and market players in the field of predictive maintenance and connected products, along with suggesting on possible business models. Alfa Laval’s expectations have been to receive anover view of the currently emerging digital ecosystem and that the project should support the fulfillment of customer needs. Interviews and research of stakeholders have been central methods during the implementation. In the project an investigation was made of different companies to design a mapping model. The study included in-person interviews with market players and end customers to support the survey on cases in which connectivity creates customer value and also to get an overview of strategies used by different market players in digitization. The literature studies have covered topics regarding Internet of Things, Connectivity, Industry 4.0, predictive maintenance and business modelling. The result has shown that the most common customer needs are asset status monitoring, predictive maintenance, higher productivity and others, all of which are related to the customer’s desire of achieving cost reduction. For the digital ecosystem, it can be stated that the current situation is fragmented and that there is a constant emergence of new solutions with quick implementations and new players trying to position themselves in the market. The study also shows that connectivity contributes to the development of new types of services, thus causing an increased need of implementation of new service-oriented business models with new revenue streams based on subscriptions.