Abstract
In the field of infrastructures’ surveillance and protection, it is important to make decisions based on activities occurring in the environment and its local context and conditions. In this paper we use an active rule based event processing architecture in order to make sense of situations from the combination of different signals received by the rule engine. However obtaining some high level information automatically is not without risks, especially in sensitive environments, and detection mistakes can happen for various reasons: the signal’s source can be defective, whether it is human—miss-interpretation of the signal—or computed—material malfunction; the aggregation rules can be wrong syntaxically, for example when a rule will never be triggered or a situation never detected; the interpretation given to the combination of signals does not correspond to the reality on the field—because the knowledge of the rule designer is subjective or because the environment evolves over-time—the rules are therefore incorrect semantically. In this paper, a new approach is proposed to avoid the third kind of error sources. We present a hybrid machine learning technique adapted to the complexity of the rules’ representation, in order to create a system more conform to reality. The proposed approach uses a combination of an Association Rule Mining algorithm and Inductive Logic Programming for rule induction. Empirical studies on simulated datasets demonstrate how our method can contribute to sensible systems such as the security of a public or semi-public place.
Similar content being viewed by others
Notes
The scenarios are taken from the French Competitiveness Pole System@tic through the project SIC: “Securisation des Infrastructures Critiques”, 2008–2010.
References
Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: VLDB ’94: Proceedings of the 20th International Conference on VLDB. Morgan Kaufmann Publishers inc. pp 487–499
Allen JF (1981) An interval-based representation of temporal knowledge. In: IJCAI’81: Proceedings of the 7th international joint conference on Artificial intelligence. Morgan Kaufmann Publishers Inc. pp 221–226
Anicic D, Fodor P, Stuhmer R, Stojanovic N (2009) An efficient logic-based complex event processing and reactivity handling. In: International Conference on Distributed Event-based Systems (DEBS)
Biba M, Maria T, Basile A, Ferilli S, Esposito F (2006) Improving scalability in ilp incremental systems, web publication
Castro LNd, Timmis JI (2003) Artificial immune systems as a novel soft computing paradigm. Soft Comput A Fusion Found Methodol Appl 7: 526–544
Chakravarthy S, Krishnaprasad V, Anwar E, Kim SK (1994) Composite events for active databases: Semantics, contexts and detection. In: VLDB ’94: Proceedings of the 20th International Conference on Very Large Data Bases. Morgan Kaufmann Publishers Inc. pp 606–617
Coffi JR, Museux N, Marsala C (2011a) Hybrid learning system for adaptive complex event processing. In: International conference on adaptive and intelligent systems (ICAIS), Klangenfurt, Austria, pp 260–271. https://icais.uni-klu.ac.at/
Coffi JR, Marsala C, Museux N (2011b) Interval logic for design and maintenance of complex event processing systems. In: Fifth international workshop on event-driven business process management, Clermont-Ferrant, France, pp 407–413. http://icep-edbpm11.fzi.de/
Dousson C, Gaborit P, Ghallab M (1993) Situation recognition: representation and algorithms. In: Proceedings of the 13th international joint conference on Artifical intelligence, vol 1. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 166–172
Esposito F, Semeraro G, Fanizzi N, Ferilli S (2000) Multistrategy theory revision: induction and abduction in inthelex. Mach Learning 38:133–156
Esper-complex event processing (2008) http://esper.codehaus.org/
Etzion O, Niblett P (2010) Event processing in action. Manning
Fenkam P, Jazayeri M, Reif G (2004) On methodologies for constructing correct event-based applications. In: International Workshop on Distributed Event-based Systems (DEBS), pp 38–43
Gatziu S, Dittrich KR (1992) Samos: an active object–oriented database system. In: IEEE Bulletin of the TC on Data Engineering. pp 23–39
Gehani NH, Jagadish HV, Shmueli O (1992) Event specification in an active object-oriented database. SIGMOD Rec 21(2): 81–90
Kavurucu Y, Senkul P, Toroslu IH (2009) Ilp-based concept discovery in multi-relational data mining. Expert Syst Appl 26: 11418–11428
King RD, Srinivasan A, Dehaspe L (2001) Warmr: a data mining tool for chemical data. J Comput Aided Mol Design 15: 173–181
Lamma E, Mello P, Milano M, Riguzzil F (1997) Introducing abduction into (extensional) inductive logic programming systems. In: Lenzerini M (ed) AI*IA 97: Advances in artificial intelligence, Lecture Notes in Computer Science, vol 1321. Springer, Berlin, pp 183–194
Landwehr N, Kersting K, Raedt LD (2007) Integrating nave bayes and foil. J Mach Learning Res 8:481–507
Lee E, Chan K (2006) Discovering Association Patterns in Large Spatio-temporal Databases. In: Sixth IEEE International Conference on Data Mining—Workshops (ICDMW’06). pp 349–354
Li X, Wang YY, Acero A (2008) Learning query intent from regularized click graphs. In: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. SIGIR ’08, ACM, pp 339–346
Luckham D (2002) The power of events. Addison-Wesley, Boston
Museux N, Vanbockryck J (2007) Event based sensors fusion for public place surveillance. In: Proceedings, 10th International Conference on Information Fusion. pp 1–8
Pagnucco M, Rajaratnam D (2005) Inverse resolution as belief change. In: IJCAI. pp 540–545
Plotkin G (1970) A note on inductive generalisation. Mach Intell 5: 153–163
Quinlan JR, Cameron-jones RM (1995) Induction of logic programs: foil and related systems. New Gener Comput 13: 287–312
Ray O (2008) Nonmonotonic abductive inductive learning. J Appl Logic 7(3): 329–340
Rijsbergen CJV (1979) Information Retrieval, 2nd edn. Butterworth-Heinemann, Newton
Simbad 3d robot simulator (2008) http://simbad.sourceforge.net/
Srikant R, Agrawal R (1996) Mining sequential patterns: Generalizations and performance improvements. In: Advances in Database Technology EDBT ’96, Lecture Notes in Computer Science, vol 1057, chap 1. Springer, Berlin, pp 1–17
Tian Y, Hampapur A, Brown L, Feris R, Lu M, Senior A, Shu C, Zhai Y (2008) Event detection, query, and retrieval for video surveillance. In: Ma Z (ed) Artificial intelligence for miximizing content based image retrieval, Idea Group Inc
Zhai Y, Tian YL, Hampapur A (2008) Composite spatio-temporal event detection in multi-camera surveillance networks. In: Workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications (October 2008)
Acknowledgments
Jean-René Coffi is supported by the french national association for research and technology (ANRT), through the CIFRE convention num. 967/2008.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Coffi, JR., Marsala, C. & Museux, N. Adaptive complex event processing for harmful situation detection. Evolving Systems 3, 167–177 (2012). https://doi.org/10.1007/s12530-012-9052-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12530-012-9052-7