Abstract
In an interconnected world, the Systems-of-Systems (SoS) paradigm is prevalent in various domains, particularly in large-scale environments as being found in the area of critical infrastructures. Due to the characteristics of SoS and those of critical infrastructures, the realization of high level services for Operational Technology Monitoring (OTM), such as failure cause reasoning, is challenging, whereas interoperability and evolvability are most pressing. In this realm, the contribution of this paper is twofold: Firstly, we conduct a systematic literature review focusing on semantic technologies in areas like (i) semantic annotations, (ii) event log focused work in the IoT, (iii) organizational process mining, and (iv) complex event processing. Based thereupon, we elaborate towards a hybrid (semi)-automatic ontology population approach in the context of OTM by combining inductive and deductive methods.
This work is supported by the Austrian Research Promotion Agency (FFG) under grant FFG Forschungspartnerschaften 874490.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alevizos, E., et al.: Probabilistic complex event recognition: a survey. ACM Comput. Surv. (CSUR) 50(5), 1–31 (2017)
Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983)
Amato, F., et al.: Detect and correlate information system events through verbose logging messages analysis. Computing 101(7), 819–830 (2019)
Belkaroui, R. et al.: Towards events ontology based on data sensors network for viticulture domain. In: Proceedings of the International Conference on the IoT, pp. 1–7. ACM (2018)
Detro, S. et al.: Enhancing semantic interoperability in healthcare using semantic process mining. In: Proceedings of the International Conference on Information Society and Technology, pp. 80–85 (2016)
Ellinas, G. et al.: Critical infrastructure systems: basic principles of monitoring, control, and security. In: Kyriakides E., Polycarpou M. (eds.) Intelligent Monitoring, Control, and Security of CIS, pp. 1–30. Springer, Berlin (2015)
Endler, M. et al.: Towards stream-based reasoning and machine learning for IoT applications. In: Intelligent System Conference, pp. 202–209. IEEE (2017)
Ganino, G., et al.: Ontology population for open-source intelligence: a GATE-based solution. Softw. Pract. Experience 48(12), 2302–2330 (2018)
Graf, D., Schwinger, W., Kapsammer, E., Retschitzegger, W., Baumgartner, N.: Cutting a Path Through the IoT Ontology Jungle - a Meta Survey. In: International Conference on Internet of Things and Intelligence Systems. IEEE (2019)
Graf, D., Schwinger, W., Kapsammer, E., Retschitzegger, W., Pröll, B., Baumgartner, N.: Towards Operational Technology Monitoring in ITS. In: International Conference on Management of Digital Eco-Systems. ACM (2019)
Graf, D., Schwinger, W., Kapsammer, E., Retschitzegger, W., Pröll, B., Baumgartner, N.: Towards message-driven ontology population - facing challenges in real-world IoT. In: World Conference on Information Systems and Technologies, pp. 361–368. Springer, Cham (2020)
Hromic, H., et al.: Real time analysis of sensor data for the IoT by means of clustering and event processing. In: Proceedings of International Conference on Communications, pp. 685–691. IEEE (2015)
Jafari, M., et al.: Role mining in access history logs. J. Comput. Inf. Syst. Ind. Manage. Appl. 1, 258–265 (2009)
Jayawardana, V., et al.: Semi-supervised instance population of an ontology using word vector embeddings. In: Proceedings of International Conference on Advances in ICT for Emerging Regions, pp. 217–223. IEEE (2017)
Jin, T., et al.: Organizational Modeling from Event Logs. In: Proceedings of International Conference on Grid and Cooperative Computing, pp. 670–675. IEEE (2007)
J. Kim and J. Lee: OpenIoT: an open service framework for the internet of things. In: Proceedings of World Forum on IoT (WF-IoT), pp. 89–93 (2014)
Körber, M., et al.: TPStream: low-latency and high-throughput temporal pattern matching on event streams. Distrib. Parallel Databases 37, 1–52 (2019)
Lin, S., et al.: Dynamic data driven-based automatic clustering and semantic annotation for IoT sensor data. Sens. Mater. 31(6), 1789–1801 (2019)
Liu, F., et al.: Device-oriented automatic semantic annotation in IoT. J. Sensors 2017, 9589,064:1–9589,064:14 (2017)
Lubani, M., et al.: Ontology population: approaches and design aspects. J. Inf. Sci. 45(4), 502–515 (2019)
Maier, M.W.: Architecting principles for systems-of-systems. J. Int. Council Syst. Eng. 1(4), 267–284 (1998)
Matzner, M., Scholta, H.: process mining approaches to detect organizational properties in CPS. In: European Conference on Information Systems (2014)
Mehdiyev, N., et al.: Determination of rule patterns in CEP using ML techniques. Proc. Comput. Sci. 61, 395–401 (2015)
Messager, Antoine, et al.: Inferring functional connectivity from time-series of events in large scale network deployments. Trans. Netw. Serv. Manage. 16(3), 857–870 (2019)
Murray, G., et al.: The convergence of IT and OT in critical infrastructure. In: Proceedings Australian Information Security Management Conference, pp. 149–155 (2017)
Ni, Z., et al.: Mining organizational structure from workflow logs. In: Proceedings of International Conference on e-Education, Entertainment a. e-Management, pp. 222–225. IEEE (2011)
Noura, M., et al.: Interoperability in IoT infrastructure: classification, challenges, and future work. In: International Conference on IoT as a Service, pp. 11–18. Springer, Cham (2017)
Reyes-Ortiz, J., et al.: Web services ontology population through text classification. In: Proceedings of Conference on Computer Science and Information Systems, pp. 491–495. IEEE (2016)
Teymourian, K., et al.: Fusion of background knowledge and streams of events. In: Proceedings of International Conference on Distributed Event-Based Systems, pp. 302–313. ACM (2012)
Zhu, M., et al.: Service hyperlink: Modeling and reusing partial process knowledge by mining event dependencies among sensor data services. In: Proceedings of International Conference on Web Services, pp. 902–905. IEEE (2017)
Zhuge, C., Vaarandi, R.: Efficient event log mining with LogClusterC. In: Proceedings of International Conference on Big Data Security on Cloud, pp. 261–266. IEEE (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Graf, D., Schwinger, W., Retschitzegger, W., Kapsammer, E., Baumgartner, N. (2021). Event-Driven Ontology Population - from Research to Practice in Critical Infrastructure Systems. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies . WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1366. Springer, Cham. https://doi.org/10.1007/978-3-030-72651-5_39
Download citation
DOI: https://doi.org/10.1007/978-3-030-72651-5_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72650-8
Online ISBN: 978-3-030-72651-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)