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Event Recognition Challenges and Techniques: Guest Editors' Introduction

Published:07 August 2014Publication History
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References

  1. J. Allen. 1983. Maintaining knowledge about temporal intervals. Comm. ACM 26, 11 (1983), 832--843. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. M. R. Álvarez, P. Félix, P. Cariñena, and A. Otero. 2010. A data mining algorithm for inducing temporal constraint networks. In Proceedings of the International Conference on Information Processing and Management of Uncertainty (IPMU). 300--309. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. A. Artikis, C. Baber, P. Bizarro, C. C. de Wit, O. Etzion, F. Fournier, P. Goulart, A. Howes, J. Lygeros, G. Paliouras, A. Schuster, and I. Sharfman. 2014a. Scalable proactive event-driven decision-making. IEEE Technology and Society Mag.Google ScholarGoogle Scholar
  4. A. Artikis, M. Weidlich, F. Schnitzler, I. Boutsis, T. Liebig, N. Piatkowski, C. Bockermann, K. Morik, V. Kalogeraki, J. Marecek, A. Gal, S. Mannor, D. Gunopulos, and D. Kinane. 2014b. Heterogeneous stream processing and crowdsourcing for urban traffic management. In Proceedings of the International Conference on Extending Database Technology (EDBT). 712--723.Google ScholarGoogle Scholar
  5. D. Athakravi, D. Corapi, K. Broda, and A. Russo. 2013. Learning through hypothesis refinement using answer set programming. In Proceedings of the International Conference of Inductive Logic Programming (ILP).Google ScholarGoogle Scholar
  6. J. Bacon, A. I. Bejan, A. R. Beresford, D. Evans, R. J. Gibbens, and K. Moody. 2011. Using real-time road traffic data to evaluate congestion. In Dependable and Historic Computing, 93--117. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. C. Balkesen, N. Dindar, M. Wetter, and N. Tatbul. 2013. RIP: run-based intra-query parallelism for scalable complex event processing. In Proceedings of the International Conference on Distributed Event-Based Systems (DEBS). 3--14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. W. Brendel, A. Fern, and S. Todorovic. 2011. Probabilistic event logic for interval-based event recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 3329--3336. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. L. Brenna, A. J. Demers, J. Gehrke, M. Hong, J. Ossher, B. Panda, M. Riedewald, M. Thatte, and W. M. White. 2007. Cayuga: a high-performance event processing engine. In Proceedings of the ACM SIGMOD Conference. 1100--1102. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. L. Brenna, J. Gehrke, M. Hong, and D. Johansen. 2009. Distributed event stream processing with non-deterministic finite automata. In Proceedings of the International and Conference on Distributed Event-Based Systems (DEBS). Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. B. Burton, Y. Genovese, N. Rayner, R. Casonato, M. Smith, M. A. Beyer, T. Austin, B. Gassman, and D. Sommer. 2010. Pattern-based strategy technologies and business practices gain momentum. Gartner Report G00208030.Google ScholarGoogle Scholar
  12. T. Calders, N. Dexters, J. J. M. Gillis, and B. Goethals. 2014. Mining frequent itemsets in a stream. Inf. Syst. 39, 233--255. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. L. Callens, G. Carrault, M.-O. Cordier, É. Fromont, F. Portet, and R. Quiniou. 2008. Intelligent adaptive monitoring for cardiac surveillance. In Proceedings of the European Conference on Artificial Intelligence (ECAI). 653--657. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. H. Chaudet. 2006. Extending the event calculus for tracking epidemic spread. Art. Intell. Medicine 38, 2. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. D. Corapi, A. Russo, and E. Lupu. 2011. Inductive logic programming in answer set programming. In Proceedings of the International Conference on Inductive Logic Programming (ILP). 91--97. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. G. Cugola and A. Margara. 2012. Processing flows of information: From data stream to complex event processing. ACM Comput. Surv. 44, 3, 15. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. M. Denecker and A. Kakas. 2002. Abduction in logic programming. In Computational Logic: Logic Programming and Beyond, A. Kakas and F. Sadri, Eds., Lecture Notes in Computer Science, vol. 2407, Springer, 99--134. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. N. Dindar, P. M. Fischer, and N. Tatbul. 2011. DejaVu: a complex event processing system for pattern matching over live and historical data streams. In Proceedings of the Distributed Event-Based Systems (DEBS). 399--400. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. P. Domingos and D. Lowd. 2009. Markov Logic: An Interface Layer for Artificial Intelligence. Morgan & Claypool Publishers. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. C. Dousson and P. L. Maigat. 2007. Chronicle recognition improvement using temporal focusing and hierarchisation. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI). 324--329. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Y. Engel and O. Etzion. 2011. Towards proactive event-driven computing. In Proceedings of the Distributed Event-Based Systems (DEBS). 125--136. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Y. Engel, O. Etzion, and Z. Feldman. 2012. A basic model for proactive event-driven computing. In Proceedings of the Distributed Event-Based Systems (DEBS). 107--118. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Z. Feldman, F. Fournier, R. Franklin, and A. Metzger. 2013. Proactive event processing in action: a case study on the proactive management of transport processes (industry article). In Proceedings of the Distributed Event-Based Systems (DEBS). 97--106. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. A. Gal, S. Wasserkrug, and O. Etzion. 2011. Event Processing over uncertain data. In Reasoning in Event-Based Distributed Systems, S. Helmer, A. Poulovassilis, and F. Xhafa, Eds., Springer, 279--304.Google ScholarGoogle Scholar
  25. N. Giatrakos, A. Deligiannakis, M. Garofalakis, I. Sharfman, and A. Schuster. 2014. Distributed geometric query monitoring using prediction models. ACM Trans. Database Syst. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. M. Hirzel. 2012. Partition and compose: parallel complex event processing. In Proceedings of the Distributed Event-Based Systems (DEBS). 191--200. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. S. Hongeng and R. Nevatia. 2003. Large-scale event detection using semi-hidden markov models. In Proceedings of the International Conference on Computer Vision (ICCV). 1455--1462. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. A. Kembhavi, T. Yeh, and L. S. Davis. 2010. Why did the person cross the road (there)? scene understanding using probabilistic logic models and common sense reasoning. In Proceedings of the European Conference on Computer Vision (ECCV). 2, 693--706. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. D. Keren, G. Sagy, A. Abboud, D. Ben-David, A. Schuster, I. Sharfman, and A. Deligiannakis. 2014. Geometric monitoring of heterogeneous streams. IEEE Trans. Knowl. Data Eng.Google ScholarGoogle ScholarCross RefCross Ref
  30. R. Kowalski and M. Sergot. 1986. A Logic-based calculus of events. New Generation Comput. 4, 1, 67--96. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. G. T. Lakshmanan, Y. G. Rabinovich, and O. Etzion. 2009. A stratified approach for supporting high throughput event processing applications. In Proceedings of the Distributed Event-Based Systems (DEBS), A. S. Gokhale and D. C. Schmidt, Eds., ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. D. Lee and W. Lee. 2005. Finding maximal frequent itemsets over online data streams adaptively. In Proceedings of the International Conference on Data Mining (ICDM). IEEE Computer Society, 266--273. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. J. Lijffijt, P. Papapetrou, and K. Puolamäki. 2012. Size matters: Finding the most informative set of window lengths. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD). 2, 451--466. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. D. Luckham. 2002. The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems. Addison-Wesley. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. D. Maier, M. Grossniklaus, S. Moorthy, and K. Tufte. 2012. Capturing episodes: may the frame be with you. In Proceedings of the Distributed Event-Based Systems (DEBS). 1--11. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, and A. H. Byers. 2011. Big data: The next frontier for innovation, competition, and productivity.Google ScholarGoogle Scholar
  37. V. I. Morariu and L. S. Davis. 2011. Multi-agent event recognition in structured scenarios. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 3289--3296. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. S. Muggleton and L. D. Raedt. 1994. Inductive Logic Programming: Theory and Methods. J. Logic Program. 19/20, 629--679.Google ScholarGoogle ScholarCross RefCross Ref
  39. P. Natarajan and R. Nevatia. 2007. Hierarchical multi-channel hidden semi markov models. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI). 2562--2567. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. O. Papapetrou, M. N. Garofalakis, and A. Deligiannakis. 2012. Sketch-based querying of distributed sliding-window data streams. Proc. VLDB Endow. 5, 10, 992--1003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. K. Patroumpas. 2013. Multi-scale window specification over streaming trajectories. J. Spatial Info. Science 7, 1, 45--75.Google ScholarGoogle Scholar
  42. O. Ray. 2009. Nonmonotonic abductive inductive learning. J. Appl. Logic 7, 3, 329--340.Google ScholarGoogle ScholarCross RefCross Ref
  43. A. Sadilek and H. A. Kautz. 2012. Location-based reasoning about complex multi-agent behavior. J. Artif. Intell. Res. 43 (2012), 87--133. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. N. Poul Schultz-Møller, M. Migliavacca, and P. R. Pietzuch. 2009. Distributed complex event processing with query rewriting. In Proceedings of the Distributed Event-Based Systems (DEBS). Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. I. Sharfman, A. Schuster, and D. Keren. 2006. A geometric approach to monitoring threshold functions over distributed data streams. In Proceedings of the ACM SIGMOD Conference. 301--312. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. A. Skarlatidis, G. Paliouras, A. Artikis, and G. Vouros. 2014. Probabilistic event calculus for event recognition. ACM Trans. Computat. Logic. (Preprint available from http://arxiv.org/abs/1207.3270.)Google ScholarGoogle Scholar
  47. A. Vautier, M.-O. Cordier, and R. Quiniou. 2007. Towards data mining without information on knowledge structure. In Proceedings of the Principles and Practice of Knowledge Discovery in Databases (PKDD). 300--311. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. U. Vespier, S. Nijssen, and A. J. Knobbe. 2013. Mining characteristic multi-scale motifs in sensor-based time series. In Proceedings of the Conference on Knowledge Management (CIKM). 2393--2398. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. D. Vesset, M. Flemming, and M. Shirer. 2011. Worldwide decision management software 2010--2014 forecast: A fast-growing opportunity to drive the intelligent economy. IDC report 226244.Google ScholarGoogle Scholar
  50. J. Wang and P. Domingos. 2008. Hybrid markov logic networks. In Proceedings of the AAAI Conference (AAAI). 1106--1111. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. S. Wasserkrug, A. Gal, O. Etzion, and Y. Turchin. 2012. Efficient processing of uncertain events in rule-based systems. IEEE Trans. Knowl. Data Eng. 24, 1, 45--58. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. J. Xu Yu, Z. Chong, H. Lu, and A. Zhou. 2004. False positive or false negative: Mining frequent itemsets from high speed transactional data streams. In Proceedings of the International Conference on Very Large Databases (VLDB), Mario A. Nascimento, M. Tamer Özsu, Donald Kossmann, Renée J. Miller, José A. Blakeley, and K. Bernhard Schiefer, Eds., Morgan Kaufmann, 204--215. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  • Published in

    cover image ACM Transactions on Internet Technology
    ACM Transactions on Internet Technology  Volume 14, Issue 1
    Special Issue on Event Recognition
    July 2014
    161 pages
    ISSN:1533-5399
    EISSN:1557-6051
    DOI:10.1145/2659232
    • Editor:
    • Munindar P. Singh
    Issue’s Table of Contents

    Copyright © 2014 ACM

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 7 August 2014
    Published in toit Volume 14, Issue 1

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