Optimizing the Resource Allocation for Approximate Query Processing

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 186)


Query optimization techniques are a proven tool essential for high performance of the database management systems. However, in a context of data spaces or new querying paradigms, such as similarity based search, exact query evaluation is neither computationally feasible nor meaningful and approximate query evaluation is the only reasonable option. In this paper a problem of resource allocation for approximate evaluation of complex queries is considered and an approximate algorithm for an optimal resource allocation is presented, providing the best feasible quality of the output result subject to a limited total cost of a query.


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  1. 1.
    Babcock, B., Chaudhuri, S., Das, G.: Dynamic sample selection for approximate query processing. In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, SIGMOD 2003, pp. 539–550. ACM, New York (2003), doi: http://doi.acm.org/10.1145/872757.872822 CrossRefGoogle Scholar
  2. 2.
    Chaudhuri, S., Das, G., Narasayya, V.: Optimized stratified sampling for approximate query processing. ACM Trans. Database Syst. 32 (2007), doi: http://doi.acm.org/10.1145/1242524.1242526
  3. 3.
    Dell’Aquila, C., Di Tria, F., Lefons, E., Tangorra, F.: Accuracy estimation in approximate query processing. In: Proceedings of the 14th WSEAS International Conference on Computers: Part of the 14th WSEAS CSCC Multiconference, ICCOMP 2010, vol. II, pp. 452–458. World Scientific and Engineering Academy and Society (WSEAS), Stevens Point (2010), http://dl.acm.org/citation.cfm?id=1984366.1984374 Google Scholar
  4. 4.
    Epimakhov, I., Hameurlain, A., Dillon, T., Morvan, F.: Resource Scheduling Methods for Query Optimization in Data Grid Systems. In: Eder, J., Bielikova, M., Tjoa, A.M. (eds.) ADBIS 2011. LNCS, vol. 6909, pp. 185–199. Springer, Heidelberg (2011), http://dl.acm.org/citation.cfm?id=2041746.2041765 CrossRefGoogle Scholar
  5. 5.
    Hu, Y., Sundara, S., Srinivasan, J.: Supporting time-constrained sql queries in oracle. In: Proceedings of the 33rd International Conference on Very large Data Bases, VLDB 2007, pp. 1207–1218. VLDB Endowment (2007), http://dl.acm.org/citation.cfm?id=1325851.1325989
  6. 6.
    Jermaine, C., Arumugam, S., Pol, A., Dobra, A.: Scalable approximate query processing with the dbo engine. ACM Trans. Database Syst. 33, 23:1–23:54 (2008), doi: http://doi.acm.org/10.1145/1412331.1412335 Google Scholar
  7. 7.
    Jiang, Q.: A framework for supporting quality of service requirements in a data stream management system. Ph.D. thesis, Arlington, TX, USA (2005) AAI3181900Google Scholar
  8. 8.
    Kossmann, D.: The state of the art in distributed query processing. ACM Comput. Surv. 32(4), 422–469 (2000), doi: http://doi.acm.org/10.1145/371578.371598 CrossRefGoogle Scholar
  9. 9.
    Kossmann, D., Stocker, K.: Iterative dynamic programming: a new class of query optimization algorithms. ACM Trans. Database Syst. 25(1), 43–82 (2000), doi: http://doi.acm.org/10.1145/352958.352982 CrossRefGoogle Scholar
  10. 10.
    Madnick, S.E., Wang, R.Y., Lee, Y.W., Zhu, H.: Overview and framework for data and information quality research. J. Data and Information Quality 1(1), 2:1–2:22 (2009), doi: http://doi.acm.org/10.1145/1515693.1516680 Google Scholar
  11. 11.
    Pentaris, F., Ioannidis, Y.: Query optimization in distributed networks of autonomous database systems. ACM Trans. Database Syst. 31(2), 537–583 (2006), doi: http://doi.acm.org/10.1145/1138394.1138397 CrossRefGoogle Scholar
  12. 12.
    Scarcello, F., Greco, G., Leone, N.: Weighted hypertree decompositions and optimal query plans. J. Comput. Syst. Sci. 73(3), 475–506 (2007), doi: http://dx.doi.org/10.1016/j.jcss.2006.10.010 MathSciNetMATHCrossRefGoogle Scholar
  13. 13.
    Yang, R., Bhulai, S., van der Mei, R., Seinstra, F.: Optimal resource allocation for time-reservation systems. Perform. Eval. 68, 414–428 (2011), doi: http://dx.doi.org/10.1016/j.peva.2011.01.003 CrossRefGoogle Scholar
  14. 14.
    Zhang, R., Koudas, N., Ooi, B.C., Srivastava, D., Zhou, P.: Streaming multiple aggregations using phantoms. The VLDB Journal 19, 557–583 (2010), doi: http://dx.doi.org/10.1007/s00778-010-0180-z CrossRefGoogle Scholar
  15. 15.
    Zhao, H.C., Xia, C.H., Liu, Z., Towsley, D.: A unified modeling framework for distributed resource allocation of general fork and join processing networks. In: Proceedings of the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2010, pp. 299–310. ACM, New York (2010), doi:http://doi.acm.org/10.1145/1811039.1811073 Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Saint Petersburg UniversityPetersburgRussia

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