article

Query Optimization in Multidatabase Systems

Authors:

D. K. Subramanian,

K. SubramanianAuthors Info & Claims

Distributed and Parallel Databases, Volume 6, Issue 2

Pages 183 - 210

https://doi.org/10.1023/A:1008691331104

Published: 01 April 1998 Publication History

Abstract

Global query execution in a multidatabase system can be done parallelly, as all the local databases are independent. In this paper, a cost model that considers parallel execution of subqueries for a global query is developed. In order to obtain maximum parallelism in query execution, it is required to find a query execution plan that is represented in the form of a bushy tree and this query tree should be balanced to the maximal possible extent with respect to execution time. A new bottom up approach called Agglomerative Approach (AA) is proposed to construct balanced bushy trees with respect to execution time. By the deterministic nature of this approach, it generates local optimal solutions. This local minima problem will be severe in the case of graph queries, i.e., queries that are represented with a graph structure. A Simulated annealing Approach (SA) is employed to obtain a (near) optimal solution. These approaches (AA and SA) are suitable for handling on-line and off-line queries respectively. A Hybrid Approach (HA), that is an integration of AA and SA, is proposed to optimize queries for which the estimated time to be spent on optimization is known a priori. Results obtained with AA and SA on both tree and graph structured queries are presented.

References

[1]

1. E. Aarts and J. Korst, Simulated Annealing and Boltzman Machine: A Stochastic Approach to Combinatorial Optimization, John Wiley and Sons: New York, 1989.

Digital Library

[2]

2. P.M.G. Apers, A.R. Hevener, and S.B. Yao, "Optimization algorithms for distributed queries," IEEE Transactions on Software Engineering, vol. 9, no. 1, 1983.

Digital Library

[3]

3. K. Bennett, M.C. Ferris, and Y.E. Ioannidis, "A genetic algorithm for database query optimization," in 4th International Conference on Genetic Algorithms and Applications, Morgan Kaufman: San Mateo, CA, 1991.

[4]

4. Y. Breitbart, H. Gracia-Molina, and A. Silberschatz, "Overview of multidatabase transaction management," Technical Report STAN-CS-92-1432, Stanford University, May 1992.

Digital Library

[5]

5. M.W. Bright, A.R. Hurson, and S.H. Pakzad, "A taxonomy and current issues in multidatabase systems," IEEE Computer, pp. 50-60, March 1992.

Digital Library

[6]

6. D. Brill, M. Templeton, and C.T. Yu, "Distributed query processing strategies in mermaid, a frontend to data management systems," in International Conference on Data Engineering, 1984.

Digital Library

[7]

7. G.V. Bultzingsloewen, "Optimization SQL queries for parallel execution," ACM SIGMOD Record, vol. 18, no. 4, pp. 17-22, 1989.

Digital Library

[8]

8. S. Ceri and G. Pelagatti, Distributed Databases: Principles and Systems, McGraw-Hill Book Company, 1984.

Digital Library

[9]

9. A.L.P. Chen, D. Brill, M. Templeton, and C.T. Yu, "Distributed query processing in multiple database system," IEEE Journal on Selected Areas in Communications, vol. 7, no. 3, pp. 390-398, April 1989.

Digital Library

[10]

10. U. Dayal, "Query processing in multidatabase system," in Query Processing in Database Systems, W. Kim, D. Reiner, and D. Batory (Eds.), Springer Verlag, 1985, pp. 81-108.

[11]

11. D.J. Dewih and J. Goray, "Parallel database systems: The future of database processing or a passing fad," ACM SIGMOD Record, vol. 19, no. 4, pp. 104-112, 1990.

Digital Library

[12]

12. W. Du, R. Krishnamurthy, and M.C. Shan, "Query optimization in heterogeneous DBMS," in 18th International Conference on VLDB, 1992, pp. 277-291.

Digital Library

[13]

13. W. Du, M. Shan, and U. Dayal, "Reducing multidatabase query response time by tree balancing," in Proc. of the ACM SIGMOD, 1995.

Digital Library

[14]

14. A.K. Elmagarmid and M. Rusinkiewicz, "Critical issues in multidatabase systems," Information Sciences, vol. 57-58, pp. 403-424, 1991.

Digital Library

[15]

15. C. Evrendilek et al., "Query optimization in multidatabase systems," in Proc. of Next Generation Information Technologies, 1995, pp. 49-58.

[16]

16. S. Ganguly, W. Hasan, and R. Krishnamurthy, "Query optimization for parallel execution," ACM SIGMOD Record, pp. 9-18, 1992.

Digital Library

[17]

17. D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley: Reading MA, 1989.

Digital Library

[18]

18. G. Graefe, "Query evaluation techniques for large databases," ACM Computing Surveys, vol. 25, no. 2, 1993.

Digital Library

[19]

19. G. Graefe and K. Ward, "Dynamic query evaluation plans," in Proc. of ACM SIGMOD Conf., 1989.

Digital Library

[20]

20. G. Graefe and W.J. Mckenna, "The volcano optimizer generator: Extensibility and efficient search," in Proc. of IEEE Intl. Conf. on Data Engg., 1993.

Digital Library

[21]

21. T. Ibaraki and T. Kameda, "Optimal nesting for computing n-relational joins," ACM Transactions on Database Systems, vol. 9, no. 3, pp. 482-502, 1984.

Digital Library

[22]

22. Y.E. Ioannidis and E. Wong, "Query optimization by simulated annealing," in ACM SIGMOD Conference on the Management of Data, May 1987, pp. 9-22.

Digital Library

[23]

23. Y.E. Ioannidis and Y. Kang, "Randomized algorithms for optimizing large join queries," in ACM SIGMOD Conference on Management of Data, May 1990, pp. 312-321.

Digital Library

[24]

24. Y.E. Ioannidis and Y.C. Kang, "Left-deep vs. bushy trees: An analysis of strategy spaces and its implications for query optimization," in ACM SIGMOD International Conference on Management of Data, 1991, pp. 168-177.

Digital Library

[25]

25. A.K. Jain and R.C. Dubes, Algorithms for Clustering Data, Prentice Hall: Englewood Cliffs, NJ 07632, 1989.

Digital Library

[26]

26. S. Kirkpatrick, C.D. Gellat, and M.P. Vecchi, "Optimization by simulated annealing," Science, vol. 220, no. 4598, pp. 671-680, May 1983.

[27]

27. Knuth, Fundamental Algorithms, Addison Wesley, 1992.

[28]

28. R.E. Korf, "Depth first iterative deepening: An optimal admissible tree search," Artificial Intelligence, vol. 27, pp. 97-109, 1985.

Digital Library

[29]

29. R. Krishnamurthy, H. Boral, and C. Zaniolo, "Optimization of nonrecursive queries," in 12th International Conference on VLDB, Kyoto, Aug. 1986, pp. 128-137.

Digital Library

[30]

30. R.S.G. Lanzelotte and P. Valduriez, "Extending the search strategy in query optimizer," in 17th International Conference on VLDB, Sept. 1991, pp. 363-373.

Digital Library

[31]

31. E. Lim and J. Srivastava, "Query optimization and processing in federated database systems," in 2nd International Conference on Information and Knowledge Management, Washington, D.C., 1993.

Digital Library

[32]

32. G.M. Lohman, C. Mohan, L.M. Haas, D. Daniels, B.G. Lindsay, P.G. Selinger, and P.F. Wilms, "Query processing in r<sup>*</sup>, in Query Processing in Database Systems, W. Kim, D. Reiner, and D. Batory (Eds.), Springer Verlag, 1985, pp. 31-47.

[33]

33. H. Lu, M.C. Shan, and K.L. Tan, "Optimization of multi-way join queries for parallel execution," in 17th International Conference on VLDB, 1991.

Digital Library

[34]

34. H. Lu, B. Ooi, and C. Goh, "On global multidatabase query optimization," SIGMOD RECORD, vol. 21, no. 4, pp. 6-11, 1992.

Digital Library

[35]

35. L.F. Mackert and G.M. Lohman, "R<sup>*</sup> Optimizer validation and performance evaluation for distributed queries," in 12th International Conference on VLDB, Kyoto, Aug. 1986, pp. 149-157.

Digital Library

[36]

36. S. Nahar, S. Sahni, and E. Shragowitz, "Simulated annealing and combinatorial optimization," in 23rd Design Automation Conference, 1986, pp. 293-299.

Digital Library

[37]

37. S. Shekhar, J. Srivastava, and S. Dutta, "A formal model of trade-off between optimization and execution costs in semantic query optimization," in International Conference on VLDB, 1988, pp. 457-467.

Digital Library

[38]

38. J. Srivastava and Elsessor, "Optimizing multi-join queries in parallel relational databases," in 2nd International Conference on Parallel and Distributed Information Systems, 1993.

Digital Library

[39]

39. A. Swami, "Optimization of large join queries: Combining heuristics and combinatorial techniques," in ACM SIGMOD International Conference on Management of Data, 1989, pp. 367-376.

Digital Library

[40]

40. A. Swami and A. Gupta, "Optimization of large join queries," in ACM SIGMOD International Conference on Management of Data, 1988, pp. 8-17.

Digital Library

[41]

41. M. Templeton, D. Brill, A.L.P. Chen, S. Dao, and E. Lund, "Mermaid--Experiences with network operation," in Proc. of the IEEE Data Engg. Conf., 1986.

Digital Library

[42]

42. M. Templeton et al., "Mermaid--A front end to distributed heterogeneous databases," Proceedings of IEEE, May 1987, vol. 75, no. 5, pp. 695-708.

[43]

43. J. Wolf, D. Dias, and P. Yu, "An effective algorithm for parallelizing sort merge joins in the presence of data skew," in Proc. of the 2nd Intl. Symp. on Databases in Parallel and Distributed Systems, 1990.

Digital Library

[44]

44. C.T. Yu, C.C. Chang, M. Templeton, D. Brill, and E. Lund, "Query processing in a fragmented relational distributed database system: Mermaid," IEEE Transactions on Software Engineering, SE-11, Aug. 1985.

Digital Library

Cited By

Forresi CFrancia MGallinucci EGolfarelli M(2022)Cost-based Optimization of Multistore Query PlansInformation Systems Frontiers10.1007/s10796-022-10320-225:5(1925-1951)Online publication date: 4-Oct-2022
https://dl.acm.org/doi/10.1007/s10796-022-10320-2
Rahman N(2016)SQL Scorecard for Improved Stability and Performance of Data WarehousesInternational Journal of Software Innovation10.4018/IJSI.20160701024:3(22-37)Online publication date: 1-Jul-2016
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Ahmad-Kassem ABobineau CCollet CDublé EGrumbach SMa FMartinez LUbéda SDesai BPokorny JBernardino J(2012)UBIQUEST, for rapid prototyping of networking applicationsProceedings of the 16th International Database Engineering & Applications Sysmposium10.1145/2351476.2351498(187-192)Online publication date: 8-Aug-2012
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Query Optimization in Multidatabase Systems

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Published In

cover image Distributed and Parallel Databases

Distributed and Parallel Databases Volume 6, Issue 2

April 1998

94 pages

ISSN:0926-8782

Editor:
Ahmed K. Elmagarmid
Purdue Univ., Lafayette, IN

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Copyright © Copyright © 1998 Kluwer Academic Publishers.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 April 1998

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Cited By

Forresi CFrancia MGallinucci EGolfarelli M(2022)Cost-based Optimization of Multistore Query PlansInformation Systems Frontiers10.1007/s10796-022-10320-225:5(1925-1951)Online publication date: 4-Oct-2022
https://dl.acm.org/doi/10.1007/s10796-022-10320-2
Rahman N(2016)SQL Scorecard for Improved Stability and Performance of Data WarehousesInternational Journal of Software Innovation10.4018/IJSI.20160701024:3(22-37)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.4018/IJSI.2016070102
Ahmad-Kassem ABobineau CCollet CDublé EGrumbach SMa FMartinez LUbéda SDesai BPokorny JBernardino J(2012)UBIQUEST, for rapid prototyping of networking applicationsProceedings of the 16th International Database Engineering & Applications Sysmposium10.1145/2351476.2351498(187-192)Online publication date: 8-Aug-2012
https://dl.acm.org/doi/10.1145/2351476.2351498
Zhu QHaridas JHou W(2008)Query optimization via contention space partitioning and cost error controlling for dynamic multidatabase systemsDistributed and Parallel Databases10.1007/s10619-008-7025-423:2(151-188)Online publication date: 1-Apr-2008
https://dl.acm.org/doi/10.1007/s10619-008-7025-4
Fu YYang RYeh I(2004)A genetic algorithm for optimal design of an inflatable knee bolsterStructural and Multidisciplinary Optimization10.1007/s00158-003-0344-126:3-4(264-271)Online publication date: 1-Feb-2004
https://dl.acm.org/doi/10.1007/s00158-003-0344-1

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