Mining Spatio-temporal Association Rules, Sources, Sinks, Stationary Regions and Thoroughfares in Object Mobility Databases

  • Florian Verhein
  • Sanjay Chawla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3882)

Abstract

As mobile devices proliferate and networks become more location-aware, the corresponding growth in spatio-temporal data will demand analysis techniques to mine patterns that take into account the semantics of such data. Association Rule Mining has been one of the more extensively studied data mining techniques, but it considers discrete transactional data (supermarket or sequential). Most attempts to apply this technique to spatial-temporal domains maps the data to transactions, thus losing the spatio-temporal characteristics. We provide a comprehensive definition of spatio-temporal association rules (STARs) that describe how objects move between regions over time. We define support in the spatio-temporal domain to effectively deal with the semantics of such data. We also introduce other patterns that are useful for mobility data; stationary regions and high traffic regions. The latter consists of sources, sinks and thoroughfares. These patterns describe important temporal characteristics of regions and we show that they can be considered as special STARs. We provide efficient algorithms to find these patterns by exploiting several pruning properties.

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References

  1. 1.
    Shekhar, S., Huang, Y.: Discovering spatial co-location patterns:a summary of results. In: Jensen, C.S., Schneider, M., Seeger, B., Tsotras, V.J. (eds.) SSTD 2001. LNCS, vol. 2121, Springer, Heidelberg (2001)CrossRefGoogle Scholar
  2. 2.
    Huang, Y., Xiong, H., Shekhar, S., Pei, J.: Mining confident co-location rules without a support threshold. In: Proceedings of the 18th ACM Symposium on Applied Computing ACM SAC (2003)Google Scholar
  3. 3.
    Ale, J.M., Rossi, G.H.: An approach to discovering temporal association rules. In: SAC 2000: Proceedings of the 2000 ACM symposium on Applied computing, pp. 294–300. ACM Press, New York (2000)Google Scholar
  4. 4.
    Li, Y., Ning, P., Wang, X.S., Jajodia, S.: Discovering calendar-based temporal association rules. Data Knowl. Eng. 44, 193–218 (2003)CrossRefGoogle Scholar
  5. 5.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of 20th International Conference on Very Large Data Bases VLDB, pp. 487–499. Morgan Kaufmann, San Francisco (1994)Google Scholar
  6. 6.
    Mennis, J., Liu, J.: Mining association rules in spatio-temporal data. In: Proceedings of the 7th International Conference on GeoComputation (2003)Google Scholar
  7. 7.
    Tao, Y., Kollios, G., Considine, J., Li, F., Papadias, D.: Spatio-temporal aggregation using sketches. In: 20th International Conference on Data Engineering, pp. 214–225. IEEE, Los Alamitos (2004)Google Scholar
  8. 8.
    Flajolet, P., Martin, G.: Probabilistic counting algorithms for data base applications. Journal of Computer Systems Science 31, 182–209 (1985)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Tsoukatos, I., Gunopulos, D.: Efficient mining of spatiotemporal patterns. In: Jensen, C.S., Schneider, M., Seeger, B., Tsotras, V.J. (eds.) SSTD 2001. LNCS, vol. 2121, pp. 425–442. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  10. 10.
    Wang, J., Hsu, W., Lee, M.L., Wang, J.T.L.: Flowminer: Finding flow patterns in spatio-temporal databases. In: ICTAI, pp. 14–21 (2004)Google Scholar
  11. 11.
    Ishikawa, Y., Tsukamoto, Y., Kitagawa, H.: Extracting mobility statistics from indexed spatio-temporal datasets. In: STDBM, pp. 9–16 (2004)Google Scholar
  12. 12.
    Mamoulis, N., Cao, H., Kollios, G., Hadjieleftheriou, M., Tao, Y., Cheung, D.W.: Mining, indexing, and querying historical spatiotemporal data. In: KDD 2004: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 236–245. ACM Press, New York (2004)CrossRefGoogle Scholar
  13. 13.
    Verhein, F., Chawla, S.: Mining spatio-temporal association rules, sources, sinks, stationary regions and thoroughfares in object mobility databases (technical report 574). Technical report, School of IT, University of Sydney, NSW, Australia (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Florian Verhein
    • 1
  • Sanjay Chawla
    • 1
  1. 1.School of Information TechnologiesUniversity of SydneyAustralia

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