Programming and Computer Software

, Volume 39, Issue 6, pp 309–317 | Cite as

Execution and optimization techniques for approximate queries in heterogeneous systems

  • A. YaryginaEmail author


High-level queries can be used for describing scenarios of complicated analytical processing in environments of distributed heterogeneous information resources. Simultaneous abrupt increase in volume and variety of data types available for mass processing in information networks and toughening of requirements on time spent for analyzing them resulted in the need of revising the known query execution and optimization methods. In this survey, approaches to the execution and optimization of high-level precise and approximate queries are considered; unresolved problems and possible ways to solve them are also discussed.


Relational Algebra Query Optimization Query Execution Lottery Ticket Equivalent Transformation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Gray, J., The next database revolution, Proceedings of the ACM SIGMOD Int. Conf. on Management of Data (Paris, 2004), Weikum, G., König, A.C., and Deßloch, S., Eds., ACM, 2004, pp. 1–4.Google Scholar
  2. 2.
    Graefe, G., Query evaluation techniques for large databases, ACM Comput. Surv., 1993, vol. 25, no. 2, pp. 73–170.CrossRefGoogle Scholar
  3. 3.
    Codd, E.F., A relational model of data for large shared data banks, Commun. ACM, 1970, vol. 13, no. 6, pp. 377–387.CrossRefzbMATHGoogle Scholar
  4. 4.
    Darwen, H. and Date, C.J., The third manifesto, SIGMOD Record, 1995, vol. 24, no. 1, pp. 39–49.CrossRefGoogle Scholar
  5. 5.
    Ioannidis, Y.E., Query optimization, ACM Comput. Surv., 1996, vol. 28, no. 1, pp. 121–123.CrossRefGoogle Scholar
  6. 6.
    Steinbrunn, M., Moerkotte, G., and Kemper, A., Heuristic and randomized optimization for the join ordering problem, VLDBJ, 1997, vol. 6, no. 3, pp. 191–208.CrossRefGoogle Scholar
  7. 7.
    Ioannidis, Y.E., The history of histograms (abridged), VLDB, 2003, pp. 19–30.Google Scholar
  8. 8.
    Kossmann, D. and Stocker, K., Iterative dynamic programming: a new class of query optimization algorithms, ACM Trans. Database Syst., 2000, vol. 25, no. 1, pp. 43–82.CrossRefGoogle Scholar
  9. 9.
    Chaudhuri, S., Ramakrishnan, R., and Weikum, G., Integrating db and ir technologies: What is the sound of one hand clapping, CIDR, 2005, pp. 1–12.Google Scholar
  10. 10.
    Adali, S., Bonatti, P., Sapino, M.L., and Subrahmanian, V.S., A multi-similarity algebra, Proc. of the 1998 ACM SIGMOD Int. Conf. on management of data, SIGMOD’98, 1998, pp. 402–413, New York, 1998.Google Scholar
  11. 11.
    Montesi, D., Trombettam, A., and Dearnley, P.A., A similarity based relational algebra for web and multimedia data, Inf. Process. Manag., 2003, vol. 39, no. 2, pp. 307–322.CrossRefzbMATHGoogle Scholar
  12. 12.
    Ciaccia, P., Montesi, D., Penzo, W., and Trombettam, A., Imprecision and user preferences in multimedia queries: A generic algebraic approach, Proc. of the-First Int. Symposium on Foundations of Information and Knowledge Systems, FoIKS’00, London, 2000, pp. 50–71.CrossRefGoogle Scholar
  13. 13.
    Schmitt, I. and Schulz, N., Similarity relational calculus and its reduction to a similarity algebra, Lecture Notes in Computer Science, 2004, vol. 2942, pp. 252–272.CrossRefGoogle Scholar
  14. 14.
    Atnafu, S., Brunie, L., and Kosch, H., Similarity-based algebra for multimedia database systems, Proc of ADC, 2001, pp. 115–122.Google Scholar
  15. 15.
    Budíková, P., Batko, M., and Zezula, P., Query language for complex similarity queries, Lecture Notes in Computer Science, 2012, vol. 7503, pp. 85–98.CrossRefGoogle Scholar
  16. 16.
    Li, C., Chen-Chuan Chang, K., Ilyas, I.F., and Song, S., Ranksql: Query algebra and optimization for relational top-k queries, Proc. of SIGMOD Conf., 2005, pp. 131–142.Google Scholar
  17. 17.
    Fagin, R., Fuzzy queries in multimedia database systems. Proc. of the seventeenth ACM SIGACT-SIGMODSIGART Symposium on Principles of database systems, PODS’98, New York, 1998, pp. 1–10.CrossRefGoogle Scholar
  18. 18.
    Fagin, R. and Wimmers, E.L., A formula for incorporating weights into scoring rules, Theor. Comput. Sci., 2000, vol. 239, no. 2, pp. 309–338.CrossRefzbMATHMathSciNetGoogle Scholar
  19. 19.
    Hu, Y., Sundara, S., and Srinivasan, J., Supporting time-constrained sql queries in Oracle, Proc. of the 33d Int. Conf. on Very Large Data Bases, VLDB’07, Endowment, 2007, pp. 1207–1218.Google Scholar
  20. 20.
    Babcock, B., Chaudhuri, S., and Das, G., Dynamic sample selection for approximate query processing, Proc. of the 2003 ACM SIGMOD Int. Conf. on Management of Data, SIGMOD’03, New York, 2003, pp. 539–550.CrossRefGoogle Scholar
  21. 21.
    Dell’Aquila, C., DiTria, F., Lefons, E., and Tangorra, F., Accuracy estimation in approximate query processing, Proc. of the 14th WSEAS Int. Conf. on Computers: Part of the 14th WSEAS CSCC Multiconference, ICCOMP’10, Stevens Point, Wisconsin, 2010, vol. II, pp. 452–458.Google Scholar
  22. 22.
    Chaudhuri, S., Das, G., and Narasayya, V., Optimized stratified sampling for approximate query processing, ACM Trans. Database Syst., 2007, vol. 32.Google Scholar
  23. 23.
    Jermaine, C., Arumugam, S., Pol, A., and Dobra, A., Scalable approximate query processing with the dbo engine, ACM Trans. Database Syst., 2008, vol. 33, pp. 1–23.CrossRefGoogle Scholar
  24. 24.
    Fagin, R., Lotem, A., and Naor, M., Optimal aggregation algorithms for middleware, J. Comput. Syst. Sci., 2003, vol. 66, no. 4, pp. 614–656.CrossRefzbMATHMathSciNetGoogle Scholar
  25. 25.
    Theobald, M., Weikum, G., and Schenkel, R., Top-k query evaluation with probabilistic guarantees, VLDB, Nascimento, M.A., Ozsu, M.T., Kossmann, D., Miller, R.J., Blakeley, J.A., and Schiefer, K.B., Eds., Morgan Kaufmann, 2004, pp. 648–659.Google Scholar
  26. 26.
    Arai, B., Das, G., Gunopulos, D., and Koudas, N., Anytime measures for top-k algorithms, VLDB, Koch, C., Gehrke, J., Garofalakis, M.N., Srivastava, D., Aberer, K., Deshpande, A., Florescu, D., Chan, C.Y., Ganti, V., Kanne, C.-C., Klas, W., and Neuhold, E.J., Eds., ACM, 2007, pp. 914–925.Google Scholar
  27. 27.
    Braga, D., Campi, A., Ceri, S., and Raffio, A., Joining the results of heterogeneous search engines, Inf. Syst., 2008., vol. 33, nos. 7–8, pp. 658–680.CrossRefGoogle Scholar
  28. 28.
    Deshpande, A., Ives, Z.G., and Raman, V., Adaptive query processing, Foundations Trends Databases, 2007, vol. 1, no. 1, pp. 1–140.CrossRefzbMATHGoogle Scholar
  29. 29.
    Babu, S., Bizarro, P., and DeWitt, D., Proactive reoptimization, Proc. of the 2005 ACM SIGMOD Int. Conf. on Management of Data, SIGMOD’05, New York, 2005, pp. 107–118.CrossRefGoogle Scholar
  30. 30.
    Eurviriyanukul, K., Paton, N.W., Fernandes, A.A.A., and Lynden, S.J., Adaptive join processing in pipelined plans, Proc. of the 13th Int. Conf. on Extending Database Technology, EDBT’10, New York, 2010, pp. 183–194.CrossRefGoogle Scholar
  31. 31.
    Markl, V., Raman, V., Simmen, D., Lohman, G., Pirahesh, H., and Cilimdzic, M., Robust query processing through progressive optimization, Proc. of the 2004 ACM SIGMOD Int. Conf. on Management of data, SIGMOD’04, New York, 2004, pp. 659–670.CrossRefGoogle Scholar
  32. 32.
    Graefe, G., New algorithms for join and grouping operations, Comput. Sci., 2012, vol. 27, no. 1, pp. 3–27.Google Scholar
  33. 33.
    Lengu, R., Missier, P., Fernandes, A.A.A., Guerrini, G., and Mesiti, M., Time-completeness trade-offs in record linkage using adaptive query processing, Proceedings of the 12th Int. Conf. on Extending Database Technology, EDBT2009 (Saint Petersburg, 2009), Kersten, M.L., Novikov, B., Teubner, J., Polutin, V., and Manegold, S., Eds., ACM, 2009, pp. 851–861.Google Scholar
  34. 34.
    Ilyas, I.F., Aref, W.G., Elmagarmid, A.K., Elmongui, H.G., Shah, R., and Vitter, J.S., Adaptive rankaware query optimization in relational databases, ACM Trans. Database Syst., 2006, vol. 31, no. 4, pp. 1257–1304.CrossRefGoogle Scholar
  35. 35.
    Farag, F., Hammad, M.A., and Alhajj, R., Adaptive query processing in data stream management systems under limited memory resources, PIKM, Nica, A. and Varde, A.S., Eds., ACM, 2010, pp. 9–16.CrossRefGoogle Scholar
  36. 36.
    Proceedings of the 12th Int. Conf. on Extending Database Technology, EDBT2009 (Saint Petersburg, 2009), Kersten, M.L., Novikov, B., Teubner, J., Polutin, V., and Manegold, S., Eds., ACM, 2009.Google Scholar
  37. 37.
    Ilyas, I.F., Shah, R., Aref, W.G., Vitter, J.S., and Elmagarmid, A.K., Rank-aware query optimization, Proceedings of the ACM SIGMOD Int. Conf. on Management of Data (Paris, 2004), Weikum, G., König, A.C., and Deßloch, S., Eds., ACM, 2004, pp. 203–214.Google Scholar
  38. 38.
    Proceedings of the ACM SIGMOD Int. Conf. on Management of Data (Paris, 2004), Weikum, G., König, A.C., and Deßloch, S., Eds., ACM, 2004.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2013

Authors and Affiliations

  1. 1.St. Petersburg State UniversitySt. PetersburgRussia

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