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Automated discovery and integration of semantic urban data streams: the ACEIS middleware
Gao, Feng; Muhammad, Intizar Ali; Curry, Edward; Mileo, Alessandra
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.
Keyword(s): Information technology; Artificial intelligence; Semantic Web; Complex Event Processing; Service Oriented Computing; RDF Stream Processing
Publication Date:
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Institution: Dublin City University
Citation(s): Gao, Feng, Muhammad, Intizar Ali, Curry, Edward and Mileo, Alessandra (2017) Automated discovery and integration of semantic urban data streams: the ACEIS middleware. Future Generation Computer Systems, 76 . pp. 561-581. ISSN 0167-739X
Publisher(s): Elsevier
File Format(s): application/pdf
Related Link(s):,
First Indexed: 2017-11-30 06:05:58 Last Updated: 2019-02-09 06:15:37