Hostname: page-component-8448b6f56d-xtgtn Total loading time: 0 Render date: 2024-04-16T12:28:44.044Z Has data issue: false hasContentIssue false
  • English
  • Français

Online Event Recognition from Moving Vehicles: Application Paper

Published online by Cambridge University Press:  20 September 2019

EFTHIMIS TSILIONIS Open the ORCID record for EFTHIMIS TSILIONIS [Opens in a new window] ,
NIKOLAOS KOUTROUMANIS ,
PANAGIOTIS NIKITOPOULOS ,
CHRISTOS DOULKERIDIS  and
ALEXANDER ARTIKIS
EFTHIMIS TSILIONIS
Affiliation:
National Center for Scientific Research ‘Demokritos’, Athens, Greece, (e-mail: eftsilio@iit.demokritos.gr)
NIKOLAOS KOUTROUMANIS
Affiliation:
University of Piraeus, Piraeus, Greece, (e-mails: koutroumanis@unipi.gr, nikp@unipi.gr, cdoulk@unipi.gr)
PANAGIOTIS NIKITOPOULOS
Affiliation:
University of Piraeus, Piraeus, Greece, (e-mails: koutroumanis@unipi.gr, nikp@unipi.gr, cdoulk@unipi.gr)
CHRISTOS DOULKERIDIS
Affiliation:
University of Piraeus, Piraeus, Greece, (e-mails: koutroumanis@unipi.gr, nikp@unipi.gr, cdoulk@unipi.gr)
ALEXANDER ARTIKIS
Affiliation:
National Center for Scientific Research ‘Demokritos’, Athens, Greece, and University of Piraeus, Piraeus, Greece, (e-mail: a.artikis@unipi.gr)
Get access Rights & Permissions [Opens in a new window]

Abstract

We present a system for online composite event recognition over streaming positions of commercial vehicles. Our system employs a data enrichment module, augmenting the mobility data with external information, such as weather data and proximity to points of interest. In addition, the composite event recognition module, based on a highly optimised logic programming implementation of the Event Calculus, consumes the enriched data and identifies activities that are beneficial in fleet management applications. We evaluate our system on large, real-world data from commercial vehicles, and illustrate its efficiency.

Keywords

Event Pattern MatchingEvent CalculusData Enrichment
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 19 , Special Issue 5-6: 35th International Conference on Logic Programming , September 2019 , pp. 841 - 856
Copyright
© Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alevizos, E., Skarlatidis, A., Artikis, A., and Paliouras, G. 2017. Probabilistic complex event recognition: A survey. ACM Comput. Surv. 50, 5, 71:1–71:31.Google Scholar
Artikis, A. and Sergot, M. J. 2010. Executable specification of open multi-agent systems. Logic Journal of the IGPL 18, 1, 31–65.CrossRefGoogle Scholar
Artikis, A., Sergot, M. J., and Paliouras, G. 2015. An event calculus for event recognition. IEEE Trans. Knowl. Data Eng. 27, 4, 895–908.Google Scholar
Artikis, A., Weidlich, M., Gal, A., Kalogeraki, V., and Gunopulos, D. 2013. Self-adaptive event recognition for intelligent transport management. In Proceedings of the IEEE International Conference on Big Data. 319–325.Google Scholar
Beck, H., Dao-Tran, M., and Eiter, T. 2018. LARS: A logic-based framework for analytic reasoning over streams. Artif. Intell. 261, 16–70.Google Scholar
Cervesato, I. and Montanari, A. 2000. A calculus of macro-events: Progress report. In Seventh International Workshop on Temporal Representation and Reasoning, TIME 2000, Nova Scotia, Canada, July 7-9, 2000. 47–58.Google Scholar
Chittaro, L. and Montanari, A. 1996. Efficient temporal reasoning in the cached event calculus. Computational Intelligence 12, 359–382.Google Scholar
Cugola, G. and Margara, A. 2010. TESLA: a formally defined event specification language. In Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems, DEBS 2010, Cambridge, United Kingdom, July 12-15, 2010. 50–61.Google Scholar
Cugola, G. and Margara, A. 2012. Processing flows of information: From data stream to complex event processing. ACM Comput. Surv. 44, 3, 15:1–15:62.Google Scholar
Demers, A. J., Gehrke, J., Panda, B., Riedewald, M., Sharma, V., and White, W. M. 2007. Cayuga: A general purpose event monitoring system. In CIDR 2007, Third Biennial Conference on Innovative Data Systems Research, Asilomar, CA, USA, January 7-10, 2007, Online Proceedings. 412–422.Google Scholar
Dong, X. L. and Srivastava, D. 2015. Big Data Integration. Synthesis Lectures on Data Management. Morgan & Claypool Publishers.Google Scholar
Dousson, C. and Maigat, P. L. 2007. Chronicle recognition improvement using temporal focusing and hierarchization. In IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, January 6-12, 2007. 324–329.Google Scholar
Giatrakos, N., Alevizos, E., Artikis, A., Deligiannakis, A., and Garofalakis, M. 2019. Complex event recognition in the big data era. VLDB Journal.Google Scholar
Grez, A., Riveros, C., and Ugarte, M. 2019. A formal framework for complex event processing. In 22nd International Conference on Database Theory, ICDT 2019, March 26-28, 2019, Lisbon, Portugal. 5:1–5:18.Google Scholar
Jacox, E. H. and Samet, H. 2007. Spatial join techniques. ACM Trans. Database Syst. 32, 1, 7.Google Scholar
Koutroumanis, N., Santipantakis, G. M., Glenis, A., Doulkeridis, C., and Vouros, G. A. 2019. Integration of mobility data with weather information. In Proceedings of the Workshops of the EDBT/ICDT 2019 Joint Conference, EDBT/ICDT 2019, Lisbon, Portugal, March 26, 2019.Google Scholar
Kowalski, R. A. and Sergot, M. J. 1986. A logic-based calculus of events. New Generation Comput. 4, 1, 67–95.Google Scholar
Liu, M., Rundensteiner, E. A., Greenfield, K., Gupta, C., Wang, S., Ari, I., and Mehta, A. 2011. E-cube: multi-dimensional event sequence analysis using hierarchical pattern query sharing. In Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2011, Athens, Greece, June 12-16, 2011. 889–900.Google Scholar
Mei, Y. and Madden, S. 2009. Zstream: a cost-based query processor for adaptively detecting composite events. In Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2009, Providence, Rhode Island, USA, June 29 - July 2, 2009. 193–206.Google Scholar
Michelioudakis, E., Artikis, A., and Paliouras, G. 2019. Semi-supervised online structure learning for composite event recognition. Machine Learning 108, 7, 1085–1110.CrossRefGoogle Scholar
Miller, R. and Shanahan, M. 2002. Some alternative formulations of the event calculus. In Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II. 452–490.Google Scholar
Montali, M., Maggi, F. M., Chesani, F., Mello, P., and van der Aalst, W. M. P. 2013. Monitoring business constraints with the event calculus. ACM TIST 5, 1, 17:1–17:30.Google Scholar
Paschke, A. 2006. Eca-ruleml: An approach combining ECA rules with temporal interval-based KR event/action logics and transactional update logics. CoRR abs/cs/0610167.Google Scholar
Paschke, A. and Bichler, M. 2008. Knowledge representation concepts for automated SLA management. Decision Support Systems 46, 1, 187–205.Google Scholar
Patroumpas, K., Alevizos, E., Artikis, A., Vodas, M., Pelekis, N., and Theodoridis, Y. 2017. Online event recognition from moving vessel trajectories. GeoInformatica 21, 2, 389–427.Google Scholar
Schultz-Møller, N. P., Migliavacca, M., and Pietzuch, P. R. 2009. Distributed complex event processing with query rewriting. In Proceedings of the Third ACM International Conference on Distributed Event-Based Systems, DEBS 2009, Nashville, Tennessee, USA, July 6-9, 2009. Google Scholar
Tsilionis, E., Artikis, A., and Paliouras, G. 2019. Incremental event calculus for run-time reasoning. In Proceedings of the 13th ACM International Conference on Distributed and Event-based Systems, DEBS 2019, Darmstadt, Germany, June 24-28, 2019. 79–90.Google Scholar
Vlassopoulos, C. and Artikis, A. 2017. Towards A simple event calculus for run-time reasoning. In Proceedings of the Thirteenth International Symposium on Commonsense Reasoning, COMMONSENSE 2017, London, UK, November 6-8, 2017.Google Scholar
Zhang, H., Diao, Y., and Immerman, N. 2014. On complexity and optimization of expensive queries in complex event processing. In International Conference on Management of Data, SIGMOD 2014, Snowbird, UT, USA, June 22-27, 2014. 217–228.Google Scholar