IBM Skip to main content
  Home     Products & services     Support & downloads     My account  
  Select a country  
Journals Home  
  Systems Journal  
  ·  Current Issue  
  ·  Recent Issues  
  ·  Papers in Progress  
  ·  Search/Index  
  ·  Orders  
  ·  Description  
  ·  Author's Guide  
Journal of Research
and Development
  Staff  
  Contact Us  
  Related link:  
     IBM Research: AI  
IBM Systems Journal  
Volume 41, Number 3, 2002
Artificial Intelligence
 Table of contents: arrowHTML arrowPDF arrowASCII   This article: arrowHTML arrowPDF arrowASCII arrowCopyright info
   

Predictive algorithms in the management of computer systems - References
by R. Vilalta, C. V. Apte, J. L. Hellerstein, S. Ma, and S. M. Weiss

Cited references

  1. R. Wolski, “Forecasting Network Performance to Support Dynamic Scheduling Using the Network Weather Service,” Proceedings of the Sixth IEEE International Symposium on High Performance Distributed Computing, IEEE, New York (1997).
  2. K. C. Claffy, H.-W. Braun, and G. C. Polyzos, “Tracking Long-term Growth of the NSFNET,” Communications of the ACM 40, No. 11, 34–45 (1994).
  3. T.-Y. Yeh and Y. N. Patt, “Alternative Implementation of Two-Level Adaptive Branch Predictions,” Proceedings of the 19th Annual International Symposium on Computer Architecture, Gold Coast, Australia, 1992, ACM, New York (1992), pp. 124–134.
  4. B. Calder, D. Grunwald, and J. Emer, “A System Level Perspective on Branch Architecture Performance,” Proceedings of the 28th Annual IEEE/ACM International Symposium on Microarchitecture, Ann Arbor, MI, 1995, IEEE, New York (1995), pp. 199–206.
  5. B. Calder and D. Grunwald, “Next Cache Line and Set Prediction,” Proceedings of the 22nd Annual International Symposium on Computer Architecture, ACM, New York (1995), pp. 287–296.
  6. N. P. Jouppi and P. Ranganathan, “The Relative Importance of Memory Latency, Bandwidth, and Branch Limits to Performance,” Workshop on Mixing Logic and DRAM: Chips that Compute and Remember (1997).
  7. Z. Xu, X. Zhang, and L. Sun, Semi-Empirical Multiprocessor Performance Predictions, TR-96-05-01, University of Texas, San Antonio, High Performance Computation and Software Lab (1996).
  8. C.-H. Hsu and U. Kremer, A Framework for Qualitative Performance Prediction, Technical Report LCSR-TR98-363, Department of Computer Science, Rutgers University (1998).
  9. M. E. Crovella and T. J. LeBlanc, “Parallel Performance Prediction Using Lost Cycles Analysis,” Proceedings of Supercomputing 94, IEEE, New York (1994), pp. 600–609.
  10. C. Chatfield, The Analysis of Time Series: An Introduction, Chapman & Hall/CRC Press (1975).
  11. R. Agrawal and R. Srikant, “Mining Sequential Patterns,” Proceedings of the 11th International Conference on Data Engineering, IEEE, New York (1995), pp. 3–14.
  12. H. Mannila, H. Toivonen, and A. I. Verkamo, “Discovering Frequent Episodes in Sequences,” Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD'95), Montreal, Canada, 1995, AAAI Press, Menlo Park, CA (1995), pp. 210–215.
  13. R. Srikant and R. Agrawal, “Mining Sequential Patterns: Generalizations and Performance Improvements,” Proceedings of the 5th International Conference on Extending Database Technology, Springer-Verlag (1996), pp. 3–17.
  14. M. J. Zaki, “Sequence Mining in Categorical Domains: Algorithms and Applications,” Sequence Learning: Paradigms, Algorithms, and Applications, R. Sun and G. L. Giles, Editors, Lecture Notes in Artificial Intelligence, Vol. 1828, Springer-Verlag (2001), pp. 163–187.
  15. K. Ali, S. Manganaris, and R. Srikant, “Partial Classification Using Association Rules,” Proceedings of the 3rd International Conference on Knowledge Discovery in Databases and Data Mining, AAAI Press, Menlo Park, CA (1997), pp. 115–118.
  16. R. J. Bayardo, “Brute-Force Mining of High Confidence Classification Rules,” Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD'97), AAAI Press, Menlo Park, CA (1997), pp. 123–126.
  17. D. Meretakis and B. Wuthrich, “Classification as Mining and Use of Labeled Itemsets,” ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD), Philadelphia, 1999, ACM, New York (1999).
  18. W. Pijls and R. Potharst, “Classification and Target Group Selection Based upon Frequent Patterns,” Proceedings of the Twelfth Belgium-Netherlands Artificial Intelligence Conference (BNAIC'00), (2000), pp. 125–132.
  19. S. Ma and J. Hellerstein, “Mining Partially Periodic Event Patterns,” Proceedings of the International Conference on Data Engineering (ICDE), IEEE, New York (2001), pp. 205–214.
  20. R. Barlow and F. Proschan, Statistical Theory of Reliability and Life Testing, Holt Rinehart and Winston, New York (1975).
  21. R. Vilalta, C. Apte, and S. Weiss, “Operational Data Analysis: Improved Predictions Using Multi-Computer Pattern Detection,” Proceedings of the 11th IFIP/IEEE International Workshop on Distributed Systems: Operations and Management, Austin, TX, 2000, Springer-Verlag, Lecture Notes in Computer Science, Vol. 1960 (2000), pp. 37–46.
  22. S. M. Weiss and C. A. Kulikowski, Computer Systems That Learn, Morgan Kaufmann Publishers, San Mateo, CA (1991).
  23. R. S. Michalski, “A Theory and Methodology of Inductive Learning,” Machine Learning: An Artificial Intelligence Approach, R. S. Michalski, J. Carbonell, and T. Mitchell, Editors, TIOGA Publishing Co., Palo Alto, CA (1983), pp. 83–134.
  24. J. Hellerstein, F. Zhang, and P. Shahabuddin, “A Statistical Approach to Predictive Detection,” Computer Networks 35, No. 1, 77–95 (2001).
  25. G. E. P. Box and G. M. Jenkins, Time Series Analysis Forecasting and Control, Prentice Hall, Englewood Cliffs, NJ (1976).
  26. P. Hoogenboom and J. Lepreau, “Computer System Performance Problem Detection Using Time Series Model,” Proceedings of INFOCOM, Kobe, Japan, IEEE, New York (1997).
  27. M. Basseville and I. Nikiforov, Detection of Abrupt Changes: Theory and Applications, Prentice Hall, Englewood Cliffs, NJ (1993).
  28. R. Vilalta and S. Ma, Predicting Rare Events in Temporal Domains Using Associative Classification Rules, Technical Report, IBM Research, T. J. Watson Research Center, Yorktown Heights, NY (2002).
  29. G. Weiss and H. Hirsh, “Learning to Predict Rare Events in Event Sequences,” Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, AAAI Press, Menlo Park, CA (1998), pp. 359–363.
  30. R. Vilalta, S. Ma, and J. Hellerstein, “Rule Induction of Computer Events,” Proceedings of the 12th IFIP/IEEE International Workshop on Distributed Systems: Operations and Management, Springer-Verlag, Lecture Notes in Computer Science (2001).
  31. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo, “Fast Discovery of Association Rules,” Advances in Knowledge Discovery and Data Mining, U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, Editors, AAAI Press, Menlo Park, CA (1996), pp. 307–328.
  32. S. Brin, R. Motwani, and C. Silverstein, “Beyond Market Baskets: Generalizing Association Rules to Correlations,” Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD'97), AAAI Press, Menlo Park, CA (1997), pp. 265–276.
  33. A. P. White and W. Z. Liu, “Bias in Information-Based Measures in Decision Tree Induction,” Machine Learning 15, No. 3, 321–329 (1994).
About IBM | Privacy | Legal | Contact