Anwendung von Semantic-Web-Services – eine empirische Überprüfung

Haniewicz, Konstanty; Kaczmarek, Monika; Zyskowski, Dominik

doi:10.1007/s11576-007-0009-6

Cited by 15 publications

(1 citation statement)

References 26 publications

(15 reference statements)

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“…By providing an ontology for event services and allowing service providers to semantically annotate their service description documents, event services can obtain better data interoperability and facilitates automatic service discovery, composition and execution [65]. The complexity of semantic annotations may be hindering the adoption of Semantic Web Service (SWS) in the real-world [66]. To cope with this issue, several automatic annotation methods have been studied, e.g., by [67,68] and [69].…”

Section: Semantic Sensor Data Stream Discovery and Integrationmentioning

confidence: 99%

Automated discovery and integration of semantic urban data streams: The ACEIS middleware

Gao

Ali

Curry

et al. 2017

Future Generation Computer Systems

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With the growing popularity of Internet of Things (IoT) technologies and sensors deployment, more and more cities are leaning towards smart cities solutions that can leverage this rich source of streaming data to gather knowledge that can be used to solve domain-specific problems. A key challenge that needs to be faced in this respect is the ability to automatically discover and integrate heterogeneous sensor data streams on the fly for applications to use them. To provide a domain-independent platform and take full benefits from semantic technologies, in this paper we present an Automated Complex Event Implementation System (ACEIS), which serves as a middleware between sensor data streams and smart city applications. ACEIS not only automatically discovers and composes IoT streams in urban infrastructures for users' requirements expressed as complex event requests, but also automatically generates stream queries in order to detect the requested complex events, bridging the gap between high-level application users and low-level information sources. We also demonstrate the use of ACEIS in a smart travel planner scenario using real-world sensor devices and datasets.

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