An early warning system (EWS) is a core type of data driven Internet of Things (IoTs) system used for environment disaster risk and effect management. The potential benefits of using a semantic-type EWS include easier sensor and data source plug-and-play, simpler, richer, and more dynamic metadata-driven data analysis and easier service interoperability and orchestration. The challenges faced during practical deployments of semantic EWSs are the need for scalable time-sensitive data exchange and processing (especially involving heterogeneous data sources) and the need for resilience to changing ICT resource constraints in crisis zones. We present a novel IoT EWS system framework that addresses these challenges, based upon a multisemantic representation model. We use lightweight semantics for metadata to enhance rich sensor data acquisition. We use heavyweight semantics for top level W3C Web Ontology Language ontology models describing multileveled knowledge-bases and semantically driven decision support and workflow orchestration. This approach is validated through determining both system related metrics and a case study involving an advanced prototype system of the semantic EWS, integrated with a deployed EWS infrastructure.
Over the past years, herbarium collections worldwide have started to digitize millions of specimens on an industrial scale. Although the imaging costs are steadily falling, capturing the accompanying label information is still predominantly done manually and develops into the principal cost factor. In order to streamline the process of capturing herbarium specimen metadata, we specified a formal extensible workflow integrating a wide range of automated specimen image analysis services. We implemented the workflow on the basis of OpenRefine together with a plugin for handling service calls and responses. The evolving system presently covers the generation of optical character recognition (OCR) from specimen images, the identification of regions of interest in images and the extraction of meaningful information items from OCR. These implementations were developed as part of the Deutsche Forschungsgemeinschaft-funded a standardised and optimised process for data acquisition from digital images of herbarium specimens (StanDAP-Herb) Project.
Abstract. The provision of accurate, comprehensive and condensed information contained in distributed environmental information systems via public search interfaces raises several technological challenges. Our approach to tackle these challenges is based on a consequent use of ontologies. Starting with an analysis of requirements resulting from semantic search scenarios, we explain the advantages of using ontologies based on standards and aim to reuse and transform terminological systems available in the environmental domain into ontologies. We develop an architecture guided by the premise of exerting a minimum of influence on existing search infrastructures. As a consequence of using a (possibly large) number of ontologies, tools for ontology management are needed. A key argument for using ontologies is that nowadays -as an outcome of the Semantic Web initiative -very powerful processing tools are available. We elaborate ontology mapping as an example and outline how a comprehensive ontology management can be achieved.
Social media are increasingly becoming a source for event-based early warning systems in the sense that they can help to detect natural disasters and support crisis management during or after disasters. In this article the authors study the problems of analyzing multilingual twitter feeds for emergency events. Specifically, they consider tsunami and earthquakes as one possible originating cause of tsunami. Twitter messages provide testified information and help to obtain a better picture of the actual situation. Generally, local civil protection authorities and the population are likely to respond in their native language. Therefore, the present work focuses on English as “lingua franca” and on under-resourced Mediterranean languages in endangered zones, particularly Turkey, Greece, and Romania. The authors investigated ten earthquake events and defined four language-specific classifiers that can be used to detect earthquakes by filtering out irrelevant messages that do not relate to the event. The final goal is to extend this work to more Mediterranean languages and to classify and extract relevant information from tweets, translating the main keywords into English. Preliminary results indicate that such a filter has the potential to confirm forecast parameters of tsunami affecting coastal areas where no tide gauges exist and could be integrated into seismographic sensor networks.
The management of heterogeneous sensor networks is a complex task. Moreover, such networks often form an isolated system that needs to be connected to other network systems. Therefore, a system-of-systems approach is required. A sensor registry is the central part of a distributed sensor network. In this paper, we describe how to enhance sensor registries with semantic technologies in order to overcome some of their limitations. The Sensor Web Enablement standards of the Open Geospatial Consortium constitute a consistent and flexible framework in order to assure syntactical interoperability between applications, which make abundant use of data from sensor networks operated by heterogeneous stakeholders. The data model used throughout these standards is the Observation & Measurement model. The conversion into an ontology provides an easier handling of the model. In this paper, we propose the use of such an ontology as the basis of a Semantic Registry. Besides providing the information about the available sensor infrastructure and their semantically rich annotations, it can easily be extended to handle for example information about available services for data analysis or simulations. Furthermore, the resilience of the SRs is a crucial point in the system architecture to prevent it becoming a single point of failure.
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