Abstract: Despite the proliferation of Internet-of-Things (IoT) platforms for building and deploying IoT applications in the cloud, there is still no easy way to integrate heterogeneous geographically and administratively dispersed sensors and IoT services in a semantically interoperable fashion. In this paper we provide an overview of the OpenIoT project, which has developed and provided a first-of-kind open source IoT platform enabling the semantic interoperability of IoT services in the cloud. At the heart of OpenIoT lies the W3C Semantic Sensor Networks (SSN) ontology, which provides a common standards-based model for representing physical and virtual sensors. OpenIoT includes also sensor middleware that eases the collection of data from virtually any sensor, while at the same time ensuring their proper semantic annotation. Furthermore, it offers a wide range of visual tools that enable the development and deployment of IoT applications with almost zero programming. Another key feature of OpenIoT is its ability to handle mobile sensors, thereby enabling the emerging wave of mobile crowd sensing applications. OpenIoT is currently supported by an active community of IoT researchers, while being extensively used for the development of IoT applications in areas where semantic interoperability is a major concern.

Abstract: Around two thirds of the energy consumed by buildings can be traced back to the residential sectors and thus household appliances. Today, most appliances are highly intelligent and networked devices, in principle being able to form complete energy consuming, producing, and managing systems. Reducing the use of energy has therefore become a matter of managing and optimizing the energy utilization at a system level. These systems are technically very heterogeneous, and standardized interfaces on a sensor and device level are therefore needed. Many of the required standards already exist, but a common architecture does not, resulting in a too fragmented and powerless market. To enable semantic interoperability for smart appliances we therefore developed SAREF, the Smart Appliance REFerence ontology. In this paper we present SAREF and describe our experience in creating this ontology in close interaction with the industry, pointing out the lessons learned and identifying topics for follow-up actions. © Springer International Publishing Switzerland 2015.

Abstract: Existing interoperability maturity models are fragmented and only cover some interoperability aspects. This paper tentatively proposes a maturity model for enterprise interoperability which is elaborated on the basis of existing ones. It is also consistent to the Enterprise Interoperability Framework currently under the standardization process. After a brief introduction, the paper reviews existing maturity models for interoperability and recalls the basic concepts of the Enterprise Interoperability Framework. Then the proposed maturity model for enterprise interoperability is discussed in details. Metrics for determining maturity levels are presented as well. Finally the last part of the paper gives the conclusions and perspectives for future work.

Abstract: We discuss in this paper, semantic web methodologies, best practices and recommendations beyond the IERC Cluster Semantic Interoperability Best Practices and Recommendations (IERC AC4). The semantic web community designed best practices and methodologies which are unknown from the IoT community. In this paper, we synthesize and highlight the most relevant work regarding ontology methodologies, engineering, best practices and tools that could be applied to Internet of Things (IoT). To the best of our knowledge, this is the first work aiming at bridging such methodologies to the IoT community and go beyond the IERC AC4 cluster. This research is being applied to three uses cases: (1) the M3 framework assisting IoT developers in designing interoperable ontology-based IoT applications, (2) the FIESTA-IoT EU project encouraging semantic interoperability within IoT, and (3) a collaborative publication of legacy ontologies.

Abstract: Smart cities advocate future environments where sensor pervasiveness, data delivery and exchange, and information mash-up enable better support of every aspect of (social) life in human settlements. As this vision matures, evolves and is shaped against several application scenarios, and adoption perspectives, a common need for scalable, pervasive, flexible and replicable infrastructures emerges. Such a need is currently fostering new design efforts to grant performance, reuse and interoperability while avoiding knowledge silos typical of early efforts on similar top is, e.g. Automation in buildings and homes. This paper introduces a federated smart city platform (SCP) developed in the context of the ALMANAC FP7 EU project and discusses lessons learned during the first experimental application of the platform to a smart waste management scenario in a medium-sized, European city. The ALMANAC SCP aims to integrate Internet of Things (IoT), capillary networks and metro access networks to offer smart services to the citizens, and thus enable Smart City processes. The key element of the SCP is a middleware supporting semantic interoperability of heterogeneous resources, devices, services and data management. The platform is built upon a dynamic federation of private and public networks, while supporting end-to-end security and privacy. Furthermore, it also enables the integration of services that, although being natively external to the platform itself, allow enriching the set of data and information used by the Smart City applications supported.

Abstract: The Internet of Things (IoT) allows machines and devices in the world to connect with each other and generate a huge amount of data, which has a great potential to provide useful knowledge across service domains. Combining the context of IoT with semantic technologies, we can build integrated semantic systems to support semantic interoperability. In this paper, we propose an integrated semantic service platform (ISSP) to support ontological models in various IoT-based service domains of a smart city. In particular, we address three main problems for providing integrated semantic services together with IoT systems: semantic discovery, dynamic semantic representation, and semantic data repository for IoT resources. To show the feasibility of the ISSP, we develop a prototype service for a smart office using the ISSP, which can provide a preset, personalized office environment by interpreting user text input via a smartphone. We also discuss a scenario to show how the ISSP-based method would help build a smart city, where services in each service domain can discover and exploit IoT resources that are wanted across domains. We expect that our method could eventually contribute to providing people in a smart city with more integrated, comprehensive services based on semantic interoperability.

Abstract: The oneM2M standard is a global initiative led jointly by major standards organizations around the world in order to develop a unique architecture for M2M communications. Prior standards, and also oneM2M, while focusing on achieving interoperability at the communication level, do not achieve interoperability at the semantic level. An expressive ontology for IoT called IoT-O is proposed, making best use of already defined ontologies in specific domains such as sensor, observation, service, quantity kind, units, or time. IoT-O also defines some missing concepts relevant for IoT such as thing, node, actuator, and actuation. The extension of the oneM2M standard to support semantic data interoperability based on IoT-O is discussed. Finally, through comprehensive use cases, benefits of the extended standard are demonstrated, ranging from heterogeneous device interoperability to autonomic behavior achieved by automated reasoning.

Abstract: The present-day health data ecosystem comprises a wide array of complex heterogeneous data sources. A wide range of clinical, health care, social and other clinically relevant information are stored in these data sources. These data exist either as structured data or as free-text. These data are generally individual person-based records, but social care data are generally case based and less formal data sources may be shared by groups. The structured data may be organised in a proprietary way or be coded using one-of-many coding, classification or terminologies that have often evolved in isolation and designed to meet the needs of the context that they have been developed. This has resulted in a wide range of semantic interoperability issues that make the integration of data held on these different systems changing. We present semantic interoperability challenges and describe a classification of these. We propose a four-step process and a toolkit for those wishing to work more ontologically, progressing from the identification and specification of concepts to validating a final ontology. The four steps are: (1) the identification and specification of data sources; (2) the conceptualisation of semantic meaning; (3) defining to what extent routine data can be used as a measure of the process or outcome of care required in a particular study or audit and (4) the formalisation and validation of the final ontology. The toolkit is an extension of a previous schema created to formalise the development of ontologies related to chronic disease management. The extensions are focused on facilitating rapid building of ontologies for time-critical research studies.

Abstract: Distributed information infrastructures are increasingly used in the geospatial domain. In the infrastructures, data are being collected by distributed sensor services, served by distributed geospatial data services, transformed by processing services and workflows, and consumed by smart clients. Consequently, Geographical Information Systems (GISs) are moving from GISystems to GIServices. Intelligent GIServices are enriched with new capabilities including knowledge representation, semantic reasoning, automatic workflow composition, and quality and traceability. Such Intelligent GIServices facilitate information discovery and integration over the network and automate the assembly of GIServices to provide value-added products. This paper provides an overview of intelligent GIServices. The concept of intelligent GIServices is described, followed by a review of the state-of-the-art technologies and methodologies relevant to intelligent GIServices. Visions on how GIServices can perceive, reason, learn, and act intelligently are highlighted. The results can provide better services for big data processing, semantic interoperability, knowledge discovery, and cross-discipline collaboration in Earth science applications.

Abstract: Enterprise information systems are increasingly being conceived as a combination of existing systems and to work as a part of an ecosystem of software products. This change demands methods and tools to deal with the challenging semantic interoperability issues. OntoUML is a well-founded modeling language that allows modelers to formalize world-views in a technologically neutral way, aiding in the solution of such interoperability challenges. In this paper, we present an overview of the OntoUML Lightweight Editor (OLED), our model-based environment to build, evaluate and implement OntoUML models, alongside with its main features and application scenarios.

Abstract: Ontology alignment is regarded as the most perspective way to achieve semantic interoperability among heterogeneous data. The majority of state of art ontology alignment systems used one or more string similarity metrics, while the performance of these metrics were not given much attention. In this paper we flrst analyze naming variations in competing ontologies, then we evaluate a wide range of string similarity metrics, from the experimental result we can get some heuristic strategies to achieve better alignment results with regard to efiectiveness and e‐ciency.

Abstract: We review the state-of-the-art in the semantic representation of moving objects.We present the key research challenges for the semantic management of moving objects.We propose a scalable framework for the semantic management of moving objects.The framework supports a distributed storage and analysis of semantic mobility data. This position paper presents our vision for the semantic management of moving objects. We argue that exploiting semantic techniques in mobility data management can bring valuable benefits to many domains characterized by the mobility of users and moving objects in general, such as traffic management, urban dynamics analysis, ambient assisted living, emergency management, m-health, etc. We present the state-of-the-art in the domain of management of semantic locations and trajectories, and outline research challenges that need to be investigated to enable a full-fledged and intelligent semantic management of moving objects and location-based services that support smarter mobility. We propose a distributed framework for the semantic enrichment and management of mobility data and analyze the potential deployment and exploitation of such a framework.

Abstract: Smart cities are becoming more and more popular. Currently, there is no unified and interoperable system which could be reused and redeployed in future smart cities. Having an interoperable: (1) system, (2) architecture, (3) workflow to process IoT data, (4) interoperable applications and services, and (5) secure access to data is becoming essential to reduce development cost in smart cities and ensure interoperability among vertical IoT applications (i.e., domain-specific). Firstly, we survey existing work integrating semantic web technologies to Internet of Things, also called 'Semantic Web of Things' and applying it to smart cities. Secondly, we share in this paper, our vision of Semantic Web of Things applied to smart cities and highlight main research challenges. Finally, the main contribution of this work is the semantic engine applied to IoT and smart cities. Moreover, the proposed semantic engine is applied to three use cases: Machine-to-Machine Measurement (M3) framework, FIESTA-IOT EU project and VITAL EU project.

Abstract: Under the context of Industrie 4.0 (I4.0), future production systems provide balanced operations between manufacturing flexibility and efficiency, realized in an autonomous, horizontal, and decentralized item-level production control framework. Structured interoperability via precise formulations on an appropriate degree is crucial to achieve software engineering efficiency in the system life cycle. However, selecting the degree of formalization can be challenging, as it crucially depends on the desired common understanding (semantic degree) between multiple parties. In this paper, we categorize different semantic degrees and map a set of technologies in industrial automation to their associated degrees. Furthermore, we created guidelines to assist engineers selecting appropriate semantic degrees in their design. We applied these guidelines on publicly available scenarios to examine the validity of the approach, and identified semantic elements over internally developed use cases concerning plug-and-produce.

Abstract: Over the past fifteen years, our perspective on tackling information interoperability problems for web-based scholarship has evolved significantly. In this opinion piece, we look back at three efforts that we have been involved in that aptly illustrate this evolution: OAI-PMH, OAI-ORE, and Memento. Understanding that no interoperability specification is neutral, we attempt to characterize the perspectives and technical toolkits that provided the basis for these endeavors. With that regard, we consider repository-centric and web-centric interoperability perspectives, and the use of a Linked Data or a REST/HATEAOS technology stack, respectively. We also lament the lack of interoperability across nodes that play a role in web-based scholarship, but end on a constructive note with some ideas regarding a possible path forward.

Abstract: In order to create better decisions for business analytics, organizations increasingly use external data, structured, semi-structured and unstructured, in addition to the (mostly structured) internal data. Current Extract-Transform-Load (ETL) tools are not suitable for this "open world scenario" because they do not consider semantic issues in the integration process. Also, current ETL tools neither support processing semantic-aware data nor create a Semantic Data Warehouse (DW) as a semantic repository of semantically integrated data. This paper describes SETL: a (Python-based) programmable Semantic ETL framework. SETL builds on Semantic Web (SW) standards and tools and supports developers by offering a number of powerful modules, classes and methods for (dimensional and semantic) DW constructs and tasks. Thus it supports semantic-aware data sources, semantic integration, and creating a semantic DW, composed of an ontology and its instances. A comprehensive experimental evaluation comparing SETL to a solution made with traditional tools (requiring much more hand-coding) on a concrete use case, shows that SETL provides better performance, knowledge base quality and programmer productivity.

Abstract: The OER movement has tended to define "openness" in terms of access to use and reuse educational materials, and to address the geographical and financial barriers among students, teachers and self-learners with open access to high quality digital educational resources. MOOCs are the continuation of this trend of openness, innovation, and use of technology to provide learning opportunities for large numbers of learners. In the last years, the amount of Open Educational Resources on the Web has increased dramatically, especially thanks to initiatives like OpenCourseWare and other Open Educational Resources movements. The potential of this vast amount of resources is enormous. In this paper an architecture based on Semantic Web technologies and the Linked Data guidelines to support the inclusion of open materials in massive online courses is presented. Linked Data is considered as one of the most effective alternatives for creating global shared information spaces, it has become an interesting approach for discovering and enriching open educational resources data, as well as achieving semantic interoperability and re-use between multiple Open Educational Resources repositories. The notion of Linked Data refers to a set of best practices for publishing, sharing and interconnecting data in RDF format. Educational repositories managers are, in fact, realizing the potential of using Linked Data for describing, discovering, linking and publishing educational data on the Web. This work shows a data architecture based on semantic web technologies that support the discovery and inclusion of open educational materials in massive online courses in engineering education. The authors focus on a type of openness: open of contents as regards re-use and re-mix, i.e. freedom to reuse the material, to combine it with other materials, to adapt and to share it further under an open license.

Abstract: Enterprise interoperability is one of the key factors for enhancing enterprise competitiveness. Achieving enterprise interoperability is an extremely complex process which involves different technological, human and organisational elements. In this paper we present a framework to help enterprise interoperability. The framework has been developed taking into account the three domains of interoperability: Enterprise Modelling, Architecture and Platform and Ontologies. The main novelty of the framework in comparison to existing ones is that it includes a step-by-step methodology that explains how to carry out an enterprise interoperability project taking into account different interoperability views, like business, process, human resources, technology, knowledge and semantics.

Abstract: Research data management is acknowledged as an important concern for institutions and several platforms to support data deposits have emerged. In this paper we start by overviewing the current practices in the data management workflow and identifying the stakeholders in this process. We then compare four recently proposed data repository platforms—DSpace, CKAN, Zenodo and Figshare—considering their architecture, support for metadata, API completeness, as well as their search mechanisms and community acceptance. To evaluate these features, we take into consideration the identified stakeholders’ requirements. In the end, we argue that, depending on local requirements, different data repositories can meet some of the stakeholders requirements. Nevertheless, there is still room for improvements, mainly regarding the compatibility with the description of data from different research domains, to further improve data reuse.

Abstract: Today, software systems tend to be split into multiple components that can operate, both, autonomously and in a networked way, in order to pursue a common objective Such a polymorph approach to system architectures requires novel specification techniques A system-of-systems conceptualization enables addressing emergent behavior, while letting the involved components and systems operate independently of each other This property requires system interoperability and can be achieved by bigraph-based modeling A flexible abstraction mechanism as well as a strong typing of system interaction enable system interoperability in evolutionary environments Consequently, the composition and decomposition of bigraph-based models support the emergence of novel behavior We evaluate the potential of this approach based on its capability to represent system-of-systems according to the distinguishing characteristics proposed by Boardman and Sauser as well as by orchestrating two independent systems