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Database Tuning Using Online Algorithms

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Recommended Reading

  1. Aboulnaga A, Chaudhuri S. Self-tuning histograms: building histograms without looking at data. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1999.

    Google Scholar 

  2. Brown KP, Mehta M, Carey MJ, Livny M. Towards automated performance tuning for complex workloads. In: Proceedings of the 20th International Conference on Very Large Data Bases; 1994. p. 72–84.

    Google Scholar 

  3. Bruno N, Chaudhuri S. An online approach to physical design tuning. In: Proceedings of the 23rd International Conference on Data Engineering; 2007.

    Google Scholar 

  4. Bruno N, Chaudhuri S, Gravano L. STHoles: a multidimensional workload-aware histogram. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2001.

    Google Scholar 

  5. Chaudhuri S, Narasayya VR. Self-tuning database systems: a decade of progress. In: Proceedings of the 33rd International Conference on Very Large Data Bases; 2007.

    Google Scholar 

  6. Chen C-M, Roussopoulos N. Adaptive selectivity estimation using query feedback. In: Proceedinds of the ACM SIGMOD International Conference on Management of Data; 1994. p. 161–72.

    Google Scholar 

  7. Dageville B, Zait M. SQL memory management in Oracle9i. In: Proceedings of the 28th International Conference on Very Large Data Bases; 2002.

    Chapter  Google Scholar 

  8. Diao Y, Hellerstein JL, Parekh SS, Griffith R, Kaiser GE, Phung DB. Self-managing systems: a control theory foundation. In: Proceedings of the 12th IEEE International Conference on Engineering of Computer-Based Systems; 2005. p. 441–8.

    Google Scholar 

  9. Markl V, Haas PJ, Kutsch M, Megiddo N, Srivastava U, Tran TM. Consistent selectivity estimation via maximum entropy. VLDB J. 2007;16(1):55–76.

    Article  Google Scholar 

  10. Srivastava U, et al. ISOMER: consistent histogram construction using query feedback. In: Proceedings of the 22nd International Conference on Data Engineering; 2006.

    Google Scholar 

  11. Stillger M, Lohman GM, Markl V, Kandil M. LEO – DB2’s LEarning optimizer. In: Proceedings of the 27th International Conference on Very Large Data Bases; 2001. p. 19–28.

    Google Scholar 

  12. Weikum G, König AC, Kraiss A, Sinnwell M. Towards self-tuning memory management for data servers. IEEE Data Eng Bull. 1999;22(2):3–11.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Microsoft Corporation, Redmond, WA, USA

    Nicolas Bruno

  2. Microsoft Research, Microsoft Corporation, One Microsoft Way, Redmond, WA, USA

    Surajit Chaudhuri

  3. Department 5: Databases and Information Systems, Max-Planck-Institut für Informatik, Saarbrücken, Germany

    Gerhard Weikum

Authors
  1. Nicolas Bruno
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  2. Surajit Chaudhuri
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  3. Gerhard Weikum
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Corresponding author

Correspondence to Nicolas Bruno .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, GA, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, ON, Canada

    M. Tamer Özsu

Section Editor information

  1. Microsoft Research, Microsoft Corporation, One Microsoft Way, Redmond, WA, USA

    Surajit Chaudhuri

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Bruno, N., Chaudhuri, S., Weikum, G. (2018). Database Tuning Using Online Algorithms. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_335

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