Towards ontology-driven situation-aware disaster management

Moreira, João Luiz Rebelo; Pires, Luís Ferreira; Sinderen, Marten van; Costa, Paolo

doi:10.3233/ao-150155

Cited by 13 publications

(15 citation statements)

References 38 publications

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“…The conceptual part supports the modelling language constructs of the specification part, which is mapped onto the appropriate technology implementations by our MDE process, where implementation is (partially) generated by MDE transformations. Horizontal (same abstraction level) exogenous (different languages) transformations are applied at the specification level to integrate models, while vertical transformations are applied to generate code, as described by Moreira et al (2015a;2015b). For example, SAREF4health was specified (designed) with support of OntoUML, along with the model validation approach, while the RDF implementation was (partially) generated from pre-defined MDE transformations from OntoUML to RDF (Moreira et al, 2016).…”

Section: Design Methodologymentioning

confidence: 99%

“…SAREF4Health was designed in the scope of a broader development, namely the "SEmantic Modeldriven development for IoT Interoperability of emergenCy serviceS" (SEMIoTICS) framework (Moreira et al, 2015a;2015b;Moreira et al, 2018c). SEMIoTICS aims at improving the semantic interoperability of Early Warning Systems (EWS) and their components, i.e., it can be used to develop semantic interoperable IoT-based EWS for emergency notification services.…”

Section: Saref4health Designmentioning

confidence: 99%

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SAREF4health: Towards IoT standard-based ontology-driven cardiac e-health systems

Moreira

Pires

Sinderen

et al. 2020

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Recently, a number of ontology-driven healthcare systems have been leveraged by the Internet-of-Things (IoT) technologies that offer opportunities to improve abnormal situation detection when integrating medical wearables and cloud infrastructure. Usually, these systems rely on standardised IoT ontologies to represent sensor data observations. The ETSI Smart Applications REFerence ontology (SAREF) is an extensible industry-oriented standard. In this paper, we explain the need for interoperability of IoT healthcare applications and the role of standardised ontologies to achieve semantic interoperability. In particular, we discuss the verbosity problem of SAREF when used for real-time electrocardiography (ECG), emphasizing the requirement of representing time series. We compared the main ontologies in this context, according to quality, message size (payload), IoT-orientation and standardisation. Here we describe the first attempt to extend SAREF for specific e-Health use cases related to ECG data, the SAREF4health extension, which tackles the verbosity problem. Ontology-driven conceptual modelling was applied to develop SAREF4health, in which an ECG ontology grounded in the Unified Foundational Ontology (UFO), which plays the role of a reference model. The methodology was enhanced by following a standardisation procedure and considering the RDF implementation of the HL7 Fast Healthcare Interoperability Resources (FHIR) standard. The validation of SAREF4health includes the responses to competency questions, as well as the development and tests of an IoT Early Warning System prototype that uses ECG data and collision identification to detect accidents with truck drivers in a port area. This prototype integrates an existing ECG wearable with a cloud infrastructure, demonstrating the performance impact of SAREF4health considering IoT constraints. Our results show that SAREF4health enables the semantic interoperability of IoT solutions that need to deal with frequency-based time series. Design decisions regarding the trade-off between ontology quality and aggregation representation are also discussed.

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Section: Design Methodologymentioning

confidence: 99%

Section: Saref4health Designmentioning

confidence: 99%

SAREF4health: Towards IoT standard-based ontology-driven cardiac e-health systems

Moreira

Pires

Sinderen

et al. 2020

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“…Engineering (DOLCE) adalah ontologi dasar yang diperluas untuk pola diskripsi dan situasi untuk memberikan formalitas situasi kontekstual untuk mendukung desain ontologi ini [7] . General formal ontology ( GFO) juga merupakan ontologi dasar yang memberikan gagasan tentang situasi [8] . UFO dikandung dari DOLCE dan GFO untuk mendapatkan perhatian dari komunitas ontologi formal karena kemajuan yang menjanjikan pada praktik permodelan konseptual yang digerakkan oleh ontologi [9] .…”

Section: Descriptive Ontology For Linguistic and Cognitiveunclassified

“…OntoUML adalah bahasa yang matang untung menggambarkan ontologi inti , bahasa pola, dan bahasa khusus domain [12] . Yang telah diterapkan dalam beberapa penelitian sebelumnya terakit peristiwa, resiko dan rencana pada situasi darurat guna menuju manajemen bencana yan berbasis ontologi [8].…”

Section: Descriptive Ontology For Linguistic and Cognitiveunclassified

Permodelan Pengetahuan Kesiapan Penanganan Bencana Di Rumah Sakit

Hardiyanti,

Fudholi

2021

IJUBI

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Natural disasters are events that generally have a negative impact. Indonesia is one of the disaster-inclined countries. The hospital is the first referral place when disaster victims need treatment. Basically, various studies that produce knowledge are already quite widely available. But for its use in natural disasters have not been managed and applied properly. Based on this, we will build knowledge of disaster management readiness in hospitals. This research develops an ontology-based knowledge model for hospital readiness for disaster management based on the concepts of health workers, related institutions, emergency plans, and tools. The ontological modeling process in this observe includes 3 phases, namely Conceptualization, Implementation and Evaluation. The construction of ontology is primarily based totally at the effects of questionnaires which have been filled by disaster team managers from three hospitals in Central Java. The results obtained from ontological measurements made for Relationship Richness amounted to 0.68, Inheritance Richness by 0.18, and Attribute Richness by 0.04. While the results of query testing conducted using DL Query Panel is a system capable of providing answers from combined Class expressions, object property to get instances of Individual data.

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“…Therefore, ultimately, by improving the foundations of the DM core ontology, the IoT EWS using the ontology as a context model can provide a higher quality situation awareness and, thus, improved semantics. As foundations to support the choices made here we studied the Barewisean situation semantics theory, its extension for the GFO ontology (the Situoid theory), the perdurantism theory behind UFO and the Endsley`s SA theory for human factors [72]. In particular, the situoid theory [184] deals with this issue by including the concept of situoid, a perdurant element that is the composition of snapshots (situations) in time.…”

Section: Situation Foundations For Context Modellingmentioning

confidence: 99%

Semantic model-driven development for IoT interoperability of emergency services

Moreira¹

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Disaster Risk Reduction (DRR) is a systematic approach to analyze potential disasters and reduce their occurrence rate and possible impact. The main DRR component is an Early Warning System (EWS), which is a distributed information system that is able to monitor the physical world and issue warnings if abnormal situations occur. EWSs that use Internet-of-Things (IoT) technologies, so called IoT EWS, are suitable to realize (near) real-time data acquisition, risk detection and message brokering between data sources and information receivers, comprising both humans (e.g., emergency managers) and machines (e.g., sirens). Over the last years, numerous IoT EWSs were developed to monitor different types of hazards. Multi-hazard EWSs are a special class of EWS that can detect different types of situations and, if necessary, react to them. Multi-hazard EWSs require integration of existing EWSs and seamless integration with new EWSs. Interoperability of EWS components is necessary for effective integration, e.g., so that sensors, devices and platforms work with each other and with other EWSs. Although IoT technologies offer possibilities to improve the EWS efficiency and effectiveness, this potential can only be exploited if interoperability challenges are addressed at all levels. In this thesis, we focus on how to improve the semantic interoperability of IoT EWSs. Semantic interoperability refers to the ability of two or more EWSs (or EWS components) to share data elements in a prescribed format (syntax) and precise unambiguous meaning (semantics). From a literature review on semantic IoT EWS approaches, we selected the three major challenges that need to be addressed together: 1) semantic integration of a variety of data sources that make use of different standards, ontologies and data models; 2) near-real-time processing in time-and safety-critical applications; and 3) data analysis for effective situation awareness and decision support. This thesis introduces the "SEmantic Model-driven development for IoT Interoperability of emergenCy serviceS" (SEMIoTICS) framework, which is a holistic approach for semantic IoT EWS. SEMIoTICS consists of a semantic modeldriven architecture that facilitates the application of data representations, model transformations and distributed software components. SEMIoTICS is a framework that can be used to develop interoperable IoT EWSs for different domains, enabling an IoT EWS to act as a cloud-based semantic broker for situation-aware decision support. SEMIoTICS is leveraged by the adoption of ontology-driven conceptual modelling for situation-aware applications, covering both EWS design-time (specification and implementation) and runtime. Furthermore, the SEMIoTICS guides the application of the Findable, Accessible, Interoperable and Reusable (FAIR) data principles, in which the role of standardization is emphasized. SEMIoTICS was validated in the context of the H2020 INTER-IoT project, in which a semantic interoperable IoT EWS was developed to detect accident risks with trucks that deli...

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Towards ontology-driven situation-aware disaster management

Cited by 13 publications

References 38 publications

SAREF4health: Towards IoT standard-based ontology-driven cardiac e-health systems

SAREF4health: Towards IoT standard-based ontology-driven cardiac e-health systems

Permodelan Pengetahuan Kesiapan Penanganan Bencana Di Rumah Sakit

Semantic model-driven development for IoT interoperability of emergency services

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