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Genetic Algorithm for Burst Detection and Activity Tracking in Event Streams

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Parallel Problem Solving from Nature - PPSN IX (PPSN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4193))

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Abstract

We introduce a new model for detection and tracking of bursts of events in a discrete temporal sequence, its only requirement being that the time scale of events is long enough to make a discrete time description meaningful. A model for the occurrence of events using with Poisson distributions is proposed, which, applying Bayesian inference transforms into the well-known Potts model of Statistical Physics, with Potts variables equal to the Poisson parameters (frequencies of events). The problem then is to find the configuration that minimizes the Potts energy, what is achieved by applying an evolutionary algorithm specially designed to incorporate the heuristics of the model. We use it to analyze data streams of very different nature, such as seismic events and weblog comments that mention a particular word. Results are compared to those of a standard dynamic programming algorithm (Viterbi) which finds the exact solution to this minimization problem. We find that, whenever both methods reach a solution, they are very similar, but the evolutionary algorithm outperforms Viterbi’s algorithm in running time by several orders of magnitude, yielding a good solution even in cases where Viterbi takes months to complete the search.

Supported by MEC projects TIC2003-09481-C04 (LA and JJM) and BFM2003-0180 (JAC), CAM-UC3M project UC3M-FI-05-007 (JAC), and CAM project S-0505/ESP/000299 MOSSNOHO (JAC).

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References

  1. Allan, J.: Topic Detection and Tracking: Event-Based Information Organization. Kluwer Academic Publishers, Dordrecht (2002)

    Book  Google Scholar 

  2. Araujo, L., Merelo, J.J.: Automatic detection of trends in dynamical text: An evolutionary approach (2006)

    Google Scholar 

  3. Bingham, E., Kabán, A., Girolami, M.: Topic identification in dynamical text by complexity pursuit. Neural Process. Lett. 17(1), 69–83 (2003)

    Article  Google Scholar 

  4. Forney, G.D.: The Viterbi algorithm. Proceedings of The IEEE 61(3), 268–278 (1973)

    Article  MathSciNet  Google Scholar 

  5. Girolami, M., Kaban, A.: Simplicial mixtures of Markov chains: Distributed modelling of dynamic user profiles. In: Thrun, S., Saul, L., Schölkopf, B. (eds.) Advances in Neural Information Processing Systems, vol. 16, MIT Press, Cambridge (2004)

    Google Scholar 

  6. Kleinberg, J.: Bursty and hierarchical structure in streams. In: Proc. 8th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, pp. 91–101. ACM, New York (2002)

    Google Scholar 

  7. Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. In: Readings in speech recognition, pp. 267–296. Morgan Kaufmann Publishers Inc., San Francisco (1990)

    Chapter  Google Scholar 

  8. Wu, F.Y.: The potts model. Review of Modern Physics 54, 235–268 (1982)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Departamento de Sistemas Informáticos y Programación, Universidad Complutense de Madrid, Spain

    Lourdes Araujo

  2. Grupo Interdisciplinar de Sistemas Complejos (GISC), Departamento de Matemáticas, Universidad Carlos III de Madrid, Spain

    José A. Cuesta

  3. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Granada, Spain

    Juan J. Merelo

Authors
  1. Lourdes Araujo
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  2. José A. Cuesta
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  3. Juan J. Merelo
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Editor information

Editors and Affiliations

  1. Science Institute, University of Iceland, P.O. Box, Iceland

    Thomas Philip Runarsson

  2. Vorarlberg University of Applied Sciences, Hochschulstr. 1, A-6850, Dornbirn, Austria

    Hans-Georg Beyer

  3. Automated Scheduling, Optimisation and Planning Group, School of Computer Science & IT, University of Nottingham, NG8 1BB, Nottingham, UK

    Edmund Burke

  4. Depto. Arquitectura y Tecnologa de Computadores, ETS Ingeiera Informtica, C/Daniel Saucedo Aranda, s/n, 18071, Granada, Spain

    Juan J. Merelo-Guervós

  5. Colorado State University, 80523, Fort Collins, CO, USA

    L. Darrell Whitley

  6. University of Birmingham, Birmingham, UK

    Xin Yao

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© 2006 Springer-Verlag Berlin Heidelberg

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Araujo, L., Cuesta, J.A., Merelo, J.J. (2006). Genetic Algorithm for Burst Detection and Activity Tracking in Event Streams. In: Runarsson, T.P., Beyer, HG., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds) Parallel Problem Solving from Nature - PPSN IX. PPSN 2006. Lecture Notes in Computer Science, vol 4193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11844297_31

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  • DOI: https://doi.org/10.1007/11844297_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-38990-3

  • Online ISBN: 978-3-540-38991-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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