With the constant growth of Internet of Things IoT ecosystems, allowing them to interact transparently has become a major issue for both the research and the software development communities. In this paper we propose a novel approach that builds semantically interoperable ecosystems of IoT devices. The approach was evaluated in order to prove that it provides complete and correct answers without affecting the response time and that it scales linearly in large ecosystems. Discovery should enable context- and content-based searching i. A thorough analysis of the current proposals that address semantic interoperability for IoT ecosystems was presented by Zout et al. There are mainly two approaches for Web-available IoT devices based on the W3C standards that perform both context- and content-based searches: federation and centralised approaches. The centralised approaches deal with the heterogeneity of the devices by requiring them to periodically send data, which compromises the freshness of the information and requires practitioners to implement synchronization mechanisms. For this reason, this article presents an extension of Thing Descriptions called WoT-Mappings that specify how data from IoT devices can be translated to RDF Resource Description Framework on the fly, thus dealing with their heterogeneity. In addition, our proposal is fully based on standards. The goal of the first was to prove that the query answers produced by eWoT are complete, correct, and that eWoT is as efficient as a triple store when performing a context-based search.

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Semantic Web Service matchmakers are the programs or frameworks for discovering and classifying ubiquitous services in digital health ecosystems. Journal.

Ecosystems that support digital businesses maximize the economic value of network connections. This forces a shift toward platforms and ecosystems that are collaborative by nature by applying business models with multiple actors playing multiple roles. The purpose of this study is to show how the main concepts emerging from research on digital platform ecosystems DPEs could be organized in a taxonomy-based framework with different levels or dimensions of analysis.

This study discusses some of the contingencies at these different levels and argues that future research needs to study DPEs across multiple levels of analysis. While this integrative framework allows the comparison, contrast and integration of various perspectives at different levels of analysis, further theorizing will be needed to advance the DPE research.

The multidimensional framework proposed here involves the use of a multimethodological approach that incorporates a synergy of businesses, technological innovations and management methods to provide support for research in interrelationships across platform ecosystems PEs on a regular basis. This paper proposes a new PE framework by constructing a formal taxonomy model that explains a vast group of phenomena produced by the PEs.

In addition to illustrating the PE taxonomy framework, this study also proposes a clear and precise description and structuring of the information in the ecosystem domain. The PE framework assists in identification, creation, assessment and disclosure research of platform business ecosystems. Because of the large number of taxonomy concepts over , only main taxonomy fragments are shown in the paper.

A multidimensional platform ecosystem framework

Abstract The dramatic increase in the use of knowledge discovery applications requires end users to write complex database search requests to retrieve information. Such users are not only expected to grasp the structural complexity of complex databases but also the semantic relationships between data stored in databases. In order to overcome such difficulties, researchers have been focusing on knowledge representation and interactive query generation through ontologies, with particular emphasis on improving the interface between data and search requests in order to bring the result sets closer to users research requirements.

This paper discusses ontology-based information retrieval approaches and techniques by taking into consideration the aspects of ontology modelling, processing and the translation of ontological knowledge into database search requests. It also extensively compares the existing ontology-to-database transformation and mapping approaches in terms of loss of data and semantics, structural mapping and domain knowledge applicability. The research outcomes, recommendations and future challenges presented in this paper can bridge the gap between ontology and relational models to generate precise search requests using ontologies.

A Digital Transportation Ecosystem is a sub-system of the Digital Ecosystem, which uses ICT resources to facilitate transport service.

As a large number of online health resources have become available, there has been a great increase of the number of health consumers replying on online health resources available on the World Wide Web Andreassen, Bujnowska-Fedak, Chronaki, Dumitru, and Pudule, ; Fox, ; Rice, ; MacLean and Heer, It has been reported that health consumers should be able to have effective access and utilise relevant health information to meet their needs Nutbeam, ; World Health Organisation, Studies also show that most consumers lack the skills to access and use effectively online health resources Friel, Bond, and Lahoz, ; Gray, ; Jain and Bickham, ; Ratzan and Parker, ; Rowlands et al.

There have been efforts to provide access to reliable health information on the World Wide Web, and MedlinePlus and InformedHealthOnline are such examples. MedlinePlus is maintained by the National Library of Medicine and it is a Web-based consumer health information service Miller, Lacroix, and Joyce, Information in health or medical domains is critical and should be provided to health consumers without difficulty. However, the growing amount of health information on the web has increased concern about effective access to quality health information because terminology, currently used for organising health or medical information, is generated by professionals and may not be familiar to users.

The terminology gap between users’ and professionals’ vocabulary in describing medical-related web documents was also uncovered by a study on indexing consistency of social tagging in comparison with professional indexing Choi, Health consumers and healthcare professionals tend to use different terms to describe health-related concepts, for example, dry mouth vs.

This terminology gap in the health domain prevents health consumers from accessing health information relevant to their information needs. In large medical health consumer websites, it has been reported that when a consumer’s terms are different from physician-defined terms, the search returned no results, for example, heart attack vs. On the other hand, as networked information resources on the web continue to grow rapidly, digital information environments have led librarians and information professionals to manage digital resources on the web.

A Human-Centered Semantic Service Platform for the Digital Ecosystems Environment

The map shows real founders’ favorite startup locations. The size of the circle represent the popularity of a hub, while the lines show from where founders come from, who feel connected to this location. Generic selectors Exact matches only Exact matches only Search in title Search in title Search in content Search in content Search in excerpt Search in posts Search in posts Search in pages Search in pages Hidden Hidden Share your opinion and leave a review on any startup hub or accelerator.

If the place is not yet on the list, just start the survey below. Which hubs have received the strongest increase in attention last month measured by Twitter activity?

ECHAlliance Connected Health Permanent Ecosystem. Patients must be at the centre of the Ecosystem as the users of connected health services. Patients will.

Book tickets. Get involved. Intelligent Health is the only large-scale, global summit series focused purely on AI in healthcare! Our CPD accredited summits bring the global AI and health community together to advance discussions on how to apply AI and drive technological collaboration in healthcare. For , we have decided with our ecosystem, to go ahead with Intelligent Health but digitally. In a new, exciting and innovative way, that will focus on content and active participation in a democratised way, from the Global North, South, East and West — healthcare without borders.

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Towards certified open data in digital service ecosystems

Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Knowledge-Based Systems, Vol. DOI: The vision of Digital Ecosystems was initiated by the European Commission, with the purpose of constructing an information and communication technology environment to facilitate the sustainable development of small and medium enterprises.

Dong H; Hussain FK; Chang E, , ‘Semantic Web Service matchmakers: for discovering and classifying ubiquitous services in digital health ecosystems’.

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PDF | The vision of Digital Ecosystems was initiated by the European Commission, with the purpose of constructing an information and.

Springer Professional. Back to the search result list. Table of Contents. Issue archive. Hint Swipe to navigate through the articles of this issue Close hint. Abstract Transport services are a fundamental utility that drives human society. This research focuses on the selection and ranking of online transport service information. The previous research in this area has been unable to achieve satisfactory performance or give sufficient freedom to service requesters to rank services based on their preferences.

In this research, we propose a Service-Requester-Centered Service Selection and Ranking Model, guided by the philosophy of user-centered design. Implementations and empirical experiments are conducted to evaluate the three sub-models and the drawn conclusions along with directions for future work are outlined.

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“Semantic service matchmaking for Digital Health Ecosystems.”

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Digital service ecosystems provide several advantages for service Matchmaking services assist in verifying the syntax and semantics of the data, the semantic data model was developed in the Digital Health Revolution.

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CPEU3 – everything you need to know about Connected health and Ecosystem. BLEDDY