Skip to main content
SpringerLink
Log in

Parallel Online Learning of Event Definitions

  • Conference paper
  • First Online:
Inductive Logic Programming (ILP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10759))

Included in the following conference series:

Abstract

Logic-based event recognition systems infer occurrences of events in time using a set of event definitions in the form of first-order rules. The Event Calculus is a temporal logic that has been used as a basis in event recognition applications, providing among others, direct connections to machine learning, via Inductive Logic Programming (ILP). OLED is a recently proposed ILP system that learns event definitions in the form of Event Calculus theories, in a single pass over a data stream. In this work we present a version of OLED that allows for parallel, online learning. We evaluate our approach on a benchmark activity recognition dataset and show that we can reduce training times, while achieving super-linear speed-ups on some occasions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://homepages.inf.ed.ac.uk/rbf/CAVIARDATA1/.

  2. 2.

    http://potassco.sourceforge.net/.

  3. 3.

    http://akka.io/.

  4. 4.

    https://github.com/nkatzz/OLED.

  5. 5.

    http://users.iit.demokritos.gr/~nkatz/CAVIAR-theories/.

References

  1. Alevizos, E., Skarlatidis, A., Artikis, A., Paliouras, G.: Probabilistic complex event recognition: a survey. ACM Comput. Surv. (2018, to appear)

    Google Scholar 

  2. Appice, A., Ceci, M., Turi, A., Malerba, D.: Sampling very large databases for parallel and distributed relational frequent pattern discovery. In: First International Workshop on Ubiquitous Knowledge Discovery Workshop (2008)

    Google Scholar 

  3. Appice, A., Ceci, M., Turi, A., Malerba, D.: A parallel, distributed algorithm for relational frequent pattern discovery from very large data sets. Intell. Data Anal. 15(1), 69–88 (2011)

    Article  Google Scholar 

  4. Artikis, A., Sergot, M., Paliouras, G.: An event calculus for event recognition. IEEE Trans. Knowl. Data Eng. 27(4), 895–908 (2015)

    Article  Google Scholar 

  5. Artikis, A., Skarlatidis, A., Paliouras, G.: Behaviour recognition from video content: a logic programming approach. Int. J. Artif. Intell. Tools 19(2), 193–209 (2010)

    Article  Google Scholar 

  6. Artikis, A., Skarlatidis, A., Portet, F., Paliouras, G.: Logic-based event recognition. Knowl. Eng. Rev. 27(4), 469–506 (2012)

    Article  Google Scholar 

  7. Blockeel, H., De Raedt, L.: Top-down induction of first-order logical decision trees. Artif. Intell. 101(1), 285–297 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  8. De Raedt, L.: Logical and Relational Learning. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68856-3

    Book  MATH  Google Scholar 

  9. Etzion, O., Niblett, P.: Event Processing in Action. Manning Publications Co., Greenwich (2010)

    Google Scholar 

  10. Fidjeland, A.K., Luk, W., Muggleton, S.H.: Customisable multi-processor acceleration of inductive logic programming. In: Latest Advances in Inductive Logic Programming, pp. 123–141 (2014)

    Chapter  Google Scholar 

  11. Fonseca, N.A., Silva, F.M.A., Costa, V.S., Camacho, R.: A pipelined data-parallel algorithm for ILP. In: 2005 IEEE International Conference on Cluster Computing (CLUSTER 2005), Boston, Massachusetts, USA, 26–30 September 2005, pp. 1–10 (2005)

    Google Scholar 

  12. Fonseca, N.A., Srinivasan, A., Silva, F., Camacho, R.: Parallel ILP for distributed-memory architectures. Mach. Learn. 74(3), 257–279 (2009)

    Article  Google Scholar 

  13. Gama, J.: Knowledge Discovery from Data Streams. CRC Press, Florida (2010)

    Book  MATH  Google Scholar 

  14. Graham, J.H., David Page Jr., C., Kamal, A.H.: Accelerating the drug design process through parallel inductive logic programming data mining. In: 2nd IEEE Computer Society Bioinformatics Conference, CSB 2003, Stanford, CA, USA, 11–14 August 2003, pp. 400–402 (2003)

    Google Scholar 

  15. Hoeffding, W.: Probability inequalities for sums of bounded random variables. J. Am. Stat. Assoc. 58(301), 13–30 (1963)

    Article  MathSciNet  MATH  Google Scholar 

  16. Katzouris, N.: Scalable relational learning for event recognition. Ph.D. thesis, University of Athens (2017). http://users.iit.demokritos.gr/~nkatz/papers/nkatz-phd.pdf

  17. Katzouris, N., Artikis, A., Paliouras, G.: Incremental learning of event definitions with inductive logic programming. Mach. Learn. 100(2–3), 555–585 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  18. Katzouris, N., Artikis, A., Paliouras, G.: Online learning of event definitions. TPLP 16(5–6), 817–833 (2016)

    MathSciNet  MATH  Google Scholar 

  19. Kowalski, R., Sergot, M.: A logic-based calculus of events. New Gener. Comput. 4(1), 67–95 (1986)

    Article  MATH  Google Scholar 

  20. Muggleton, S.: Inverse entailment and progol. New Gener. Comput. 13(3&4), 245–286 (1995)

    Article  Google Scholar 

  21. Nishiyama, H., Ohwada, H.: Yet another parallel hypothesis search for inverse entailment. In: ILP (2015)

    Google Scholar 

  22. Ohwada, H., Mizoguchi, F.: Parallel execution for speeding up inductive logic programming systems. In: Arikawa, S., Furukawa, K. (eds.) DS 1999. LNCS (LNAI), vol. 1721, pp. 277–286. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-46846-3_25

    Chapter  Google Scholar 

  23. Paschke, A., Bichler, M.: Knowledge representation concepts for automated SLA management. Decis. Support Syst. 46(1), 187–205 (2008)

    Article  Google Scholar 

  24. Patroumpas, K., Alevizos, E., Artikis, A., Vodas, M., Pelekis, N., Theodoridis, Y.: Online event recognition from moving vessel trajectories. GeoInformatica 21(2), 389–427 (2017)

    Article  Google Scholar 

  25. Rodrigues, C., Soldano, H., Bourgne, G., Rouveirol, C.: A consistency based approach of action model learning in a community of agents. In: International conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2014, Paris, France, 5–9 May 2014, pp. 1557–1558 (2014)

    Google Scholar 

  26. Rodrigues, C., Soldano, H., Bourgne, G., Rouveirol, C.: Multi agent learning of relational action models. In: ECAI 2014 - 21st European Conference on Artificial Intelligence, 18–22 August 2014, Prague, Czech Republic - Including Prestigious Applications of Intelligent Systems (PAIS 2014), pp. 1087–1088 (2014)

    Google Scholar 

  27. Silva, A., Antunes, C.: Multi-relational pattern mining over data streams. Data Min. Knowl. Disc. 29(6), 1783–1814 (2015)

    Article  MathSciNet  Google Scholar 

  28. Skillicorn, D.B., Wang, Y.: Parallel and sequential algorithms for data mining using inductive logic. Knowl. Inf. Syst. 3(4), 405–421 (2001)

    Article  MATH  Google Scholar 

  29. Srinivasan, A., Bain, M.: Relational models with streaming ILP. In: ILP (2013)

    Google Scholar 

  30. Srinivasan, A., Faruquie, T.A., Joshi, S.: Data and task parallelism in ILP using mapreduce. Mach. Learn. 86(1), 141–168 (2012)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

This work is funded by the H2020 project datAcron (687591).

Author information

Authors and Affiliations

  1. National Center for Scientific Research “Demokritos”, Athens, Greece

    Nikos Katzouris, Alexander Artikis & Georgios Paliouras

  2. Department of Maritime Studies, University of Piraeus, Piraeus, Greece

    Alexander Artikis

Authors
  1. Nikos Katzouris
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexander Artikis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Georgios Paliouras
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nikos Katzouris .

Editor information

Editors and Affiliations

  1. University of Strasbourg, Strasbourg, France

    Nicolas Lachiche

  2. University of Orléans, Orléans, France

    Christel Vrain

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Katzouris, N., Artikis, A., Paliouras, G. (2018). Parallel Online Learning of Event Definitions. In: Lachiche, N., Vrain, C. (eds) Inductive Logic Programming. ILP 2017. Lecture Notes in Computer Science(), vol 10759. Springer, Cham. https://doi.org/10.1007/978-3-319-78090-0_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-78090-0_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78089-4

  • Online ISBN: 978-3-319-78090-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation