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Volume 41, Number 3, 2002
Artificial Intelligence
 Table of contents: arrowHTML arrowPDF arrowASCII   This article: arrowHTML arrowPDF arrowASCII arrowCopyright info
   

Discovering actionable patterns in event data - References
by J. L. Hellerstein, S. Ma, and C.-S. Perng

Cited references and notes

  1. See http://www.tivoli.com and the Tivoli TEC manual.
  2. See http://www.ca.com/products and the Computer Associates Unicenter manual.
  3. K. R. Milliken, A. V. Cruise, R. L. Ennis, A. J. Finkel, J. L. Hellerstein, D. J. Loeb, D. A. Klein, M. J. Masullo, H. M. Van Woerkom, and N. B. Waite, “YES/MVS and the Automation of Operations for Large Computer Complexes,” IBM Systems Journal 25, No. 2, 159–180 (1986).
  4. S. A. Yemini, S. Sliger, M. Eyal, Y. Yemini, and D. Ohsie, “High Speed and Robust Event Correlation,” IEEE Communications Magazine 34, No. 5, 82–90 (1996).
  5. D. Thoenen, J. Riosa, and J. L. Hellerstein, “Event Relationship Networks: A Framework for Action Oriented Analysis for Event Management,” Proceedings of the IFIP/IEEE International Symposium on Integrated Network Management, Seattle, WA, May 2001, IEEE, New York (2001), pp. 593–606.
  6. S. Ma and J. L. Hellerstein, “Eventbrowser: A Flexible Tool for Scalable Analysis of Event Data,” Proceedings of the 10th IFIP/IEEE International Workshop on Distributed Systems: Operations and Management (DSOM'99), Zurich, Switzerland, October 1999, Lecture Notes in Computer Science, Vol. 1700, Springer, New York (1999), pp. 285–296.
  7. S. Ma, C. Perng, and J. L. Hellerstein, “Eventminer: An Integrated Mining Tool for Scalable Analysis of Event Data,” Proceedings of the Visual Data Mining Workshop (KDD'01), San Francisco, August 2001, ACM, New York (2001), pp. 1–9.
  8. J. L. Hellerstein and S. Ma, “Mining Event Data for Actionable Patterns,” Proceedings of the CMG 2000 International Conference, Orlando, FL, December 2000, The Computer Measurement Group (2000).
  9. L. Burns, J. L. Hellerstein, S. Ma, C. Perng, and D. A. Rabenhorst, “A Systematic Approach to Discovering Correlation Rules for Event Management,” Proceedings of the IFIP/IEEE International Symposium on Integrated Network Management, Seattle, WA, May 2001, IEEE, New York (2001), pp. 345–359.
  10. D. Taylor, N. Halim, J. L. Hellerstein, and S. Ma, “Scalable Visualization of Event Data,” Proceedings of the 11th IFIP/IEEE International Workshop on Distributed Systems: Operations and Management (DSOM'00), Austin, TX, December 2000, Lecture Notes in Computer Science, Vol. 1960, Springer, New York (2000), pp. 47–58.
  11. R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proceedings of the 20th International Conference on Very Large Data Bases (VLDB'94), September 1994, Santiago de Chile, Chile, Morgan Kaufmann, San Francisco (1994), pp. 487–499.
  12. H. Mannila, H. Toivonen, and A. Verkamo, “Discovery of Frequent Episodes in Event Sequences,” Data Mining and Knowledge Discovery 1, No. 3, 259–289 (1997).
  13. R. Agrawal, T. Imielinski, and A. Swami, “Mining Association Rules Between Sets of Items in Large Databases,” Proceedings of the ACM SIGMOD International Conference of Management of Data, Washington, DC, May 1993, ACM, New York (1993), pp. 207–216.
  14. S. Brin, R. Motiwani, and C. Silverstein, “Beyond Market Baskets: Generalizing Association Rules to Correlations,” Data Mining and Knowledge Discovery 2, No. 1, 39–68 (1998).
  15. J. Pei and J. Han, “Can We Push More Constraints into Frequent Pattern Mining?” Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'00), Boston, MA, August 2000, ACM, New York (2000), pp. 350–354.
  16. R. C. Agarwal, C. C. Aggarwal, and V. V. V. Prasad, “Depth First Generation of Long Patterns,” Proceedings of the International Conference on Knowledge Discovery and Data Mining, Boston, MA, August 2000, ACM, New York (2000), pp. 108–118.
  17. R. J. Bayardo, “Efficiently Mining Long Patterns from Databases,” Proceedings of the ACM SIGMOD International Conference on Management of Data, Seattle, WA, June 1998, ACM, New York (1998), pp. 85–93.
  18. J. Han, J. Pei, and Y. Yin, “Mining Frequent Patterns Without Candidate Generation,” Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD'00), Dallas, TX, May 2000, ACM, New York (2000), pp. 1–12.
  19. R. Agrawal and R. Srikant, “Mining Sequential Patterns,” Proceedings of the 11th International Conference on Data Engineering, Taipei, Taiwan, March 1995, IEEE, New York (1995), pp. 3–14.
  20. C. Apte and S. J. Hong, “Predicting Equity Returns from Securities Data with Minimal Rule Generation,” Advances in Knowledge Discovery and Data Mining, U. M. Fayyad et al., Editors, AAAI/MIT Press (1984), pp. 541–560.
  21. R. Cooley, J. Srivastava, and B. Mobasher, “Web Mining: Information and Pattern Discovery on the World Wide Web,” Proceedings of the IEEE International Conference on Tools with Artificial Intelligence, Newport Beach, CA, November 1997, IEEE, New York (1997).
  22. C. Apte, S. Weiss, and G. Grout, “Predicting Defects in Disk Drive Manufacturing: A Case Study in High-Dimensional Classification,” IEEE Conference on Artificial Intelligence Applications, IEEE, New York (1993), pp. 212–218.
  23. S. Ma and J. L. Hellerstein, “Ordering Categorical Data for Best Visualization,” Proceedings of the IEEE Symposium on Information Visualization (InfoVis'99), IEEE, New York (1999), pp. 15–19.
  24. A. Beygelzimer, C. Perng, and S. Ma, “Fast Ordering of Large Categorical Datasets for Better Visualization,” Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'01), San Francisco, CA, August 2001, ACM, New York (2001).
  25. J. Han, G. Dong, and Y. Yin, “Efficient Mining of Partially Periodic Patterns in Time Series Database,” Proceedings of the 1999 International Conference on Data Engineering (ICDE'99), Sydney, Australia, March 1999, IEEE, New York (1999), pp. 106–115.
  26. S. Ma and J. L. Hellerstein, “Mining Partially Periodic Event Patterns with Unknown Periods,” Proceedings of the 2001 International Conference on Data Engineering (ICDE'01), Heidelberg, Germany, April 2001, IEEE, New York (2001), pp. 205–214.
  27. S. Ma and J. L. Hellerstein, “Mining Mutually Dependent Patterns,” Proceedings of the 2001 International Conference on Data Mining (ICDM'01), San Jose, CA, November 2001, IEEE, New York (2001), pp. 409–416.
  28. C.-S. Perng, H. Wang, S. Ma, and J. L. Hellerstein, “FARM: A Framework for Exploring Mining Spaces with Multiple Attributes,” Proceedings of the 2001 International Conference on Data Mining, San Jose, CA, November 2001, IEEE, New York (2001), pp. 449–456.
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