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

Gao, Feng, Ali, Muhammad Intizar orcid logoORCID: 0000-0002-0674-2131, Curry, Edward and Mileo, Alessandra orcid logoORCID: 0000-0002-6614-6462 (2017) Automated discovery and integration of semantic urban data streams: the ACEIS middleware. Future Generation Computer Systems, 76 . pp. 561-581. ISSN 0167-739X

Abstract
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.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Semantic Web; Complex Event Processing; Service Oriented Computing; RDF Stream Processing
Subjects:Computer Science > Information technology
Computer Science > Artificial intelligence
DCU Faculties and Centres:Research Institutes and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Publisher:Elsevier
Official URL:https://doi.org/10.1016/j.future.2017.03.002
Copyright Information:© 2017 Elsevier
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland grant No. SFI/12/RC/2289, European Science Foundation. EU FP7 CityPulse Project under grant No. 603095, Key Projects of National Social Science Foundation of China (11 & ZD189)
ID Code:21737
Deposited On:03 May 2017 11:28 by Alessandra Mileo . Last Modified 18 Apr 2023 15:53
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