Abstract
This paper addresses a semantic stream processing pipeline, including data collection, semantic annotation, RDF data storage and query processing. We investigate whether the semantic annotation step could be moved on the edge, by designing and evaluating two alternative processing architectures. Experiments show that the edge processing fulfills the low-latency requirement, facilitating the parallel processing of the semantic enrichment for the sensor data.
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References
Apache Kafka. https://kafka.apache.org. Accessed 22 May 2020
Apache Zookeeper. https://zookeeper.apache.org/. Accessed 22 May 2020
GraphDB. https://graphdb.ontotext.com/. Accessed 22 May 2020
Bellini, P., Nesi, P.: Performance assessment of RDF graph databases for smart city services. J. Vis. Lang. Comput. 45, 24–38 (2018)
Chen, J., Lécué, F., Pan, J.Z., Chen, H.: Learning from ontology streams with semantic concept drift. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 957–963. IJCAI’17, AAAI Press (2017)
Cyganiak, R., Wood, D., Lanthaler, M.: RDF 1.1 Concepts and Abstract Syntax. W3C Recommendation, 25 February 2014. https://www.w3.org/TR/rdf11-concepts/. Accessed 22 May 2020
Dell’Aglio, D., Della Valle, E., van Harmelen, F., Bernstein, A.: Stream reasoning: a survey and outlook. Data Sci. 1(1–2), 59–83 (2017)
Haller, A., Janowicz, K., Cox, S., Le-Phuoc, D., Taylor, K., Lefrançois, M.: Semantic Sensor Network Ontology. W3C Recommendation, 19 October 2017. https://www.w3.org/TR//vocab-ssn/. Accessed 22 May 2020
Le-Phuoc, D., Dao-Tran, M., Xavier Parreira, J., Hauswirth, M.: A native and adaptive approach for unified processing of linked streams and linked data. In: Aroyo, L., et al. (eds.) ISWC 2011. LNCS, vol. 7031, pp. 370–388. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25073-6_24
Le-Phuoc, D., Manfred, H.: Semantic stream processing. In: Sakr, S., Zomaya, A. (eds.) Encyclopedia of Big Data Technologies. Springer, Berlin (2019)
Li, X., Li, D., Wan, J., Vasilakos, A.V., Lai, C.F., Wang, S.: A review of industrial wireless networks in the context of industry 4.0. Wireless Netw. 23(1), 23–41 (2017)
Narkhede, N., Shapira, G., Palino, T.: Kafka: The Definitive Guide: Real-time Data and Stream Processing at Scale. O’Reilly Media, Inc., California (2017)
Pacha, S., Murugan, S.R., Sethukarasi, R.: Semantic annotation of summarized sensor data stream for effective query processing. J. Supercomput. 76, 4017–4039 (2020). https://doi.org/10.1007/s11227-017-2183-7
Ren, X., Curé, O.: Strider: a hybrid adaptive distributed RDF stream processing engine. In: d’Amato, C. et al. (eds.) ISWC 2017. LNCS, vol. 10587, pp. 559–576. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68288-4_33
Wieringa, R.J.: Design Science Methodology for Information Systems and Software engineering. Springer, Berlin (2014)
Zalhan, P.G., Silaghi, G.C., Buchmann, R.A.: Marrying big data with smart data in sensor stream processing. In: Siarheyeva, A. et al. (ed.) 28th International Conference on Information Systems Development (ISD2019). AIS eLibrary (2019)
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Zălhan, PG., Silaghi, G.C., Buchmann, R.A. (2020). Towards a Semantic Edge Processing of Sensor Data. An Incipient Experiment. In: Djemame, K., Altmann, J., Bañares, J.Á., Agmon Ben-Yehuda, O., Stankovski, V., Tuffin, B. (eds) Economics of Grids, Clouds, Systems, and Services. GECON 2020. Lecture Notes in Computer Science(), vol 12441. Springer, Cham. https://doi.org/10.1007/978-3-030-63058-4_7
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DOI: https://doi.org/10.1007/978-3-030-63058-4_7
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