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Distributed and Parallel Databases

, Volume 36, Issue 2, pp 399–441 | Cite as

Multi-join query optimization in bucket-based encrypted databases using an enhanced ant colony optimization algorithm

  • Mahmoud Jafarinejad
  • Morteza Amini
Article
  • 110 Downloads

Abstract

One of the organizations’ main concerns is to protect sensitive data in database systems, especially the ones outsourced to untrusted service providers. An effective solution for this issue is to employ database encryption methods. Among different encryption approaches, Bucket-based method has the advantage of balancing security and performance of database operations. However, generating false-positive results in executing queries is the main drawback of this method. On the other hand, multi-join queries are one of the most critical operations executed on these stored sensitive data. Hence, acceptable processing and response time in executing multi-join queries is required. In this paper, we propose an enhanced ant-colony algorithm (named BACO) which aims to reduce the required processing efforts in multi-join query optimization problem alongside with reducing the total false-positive results generated in Bucket-based encrypted databases. Our enhanced solution approach leads to much less response time without losing solutions’ quality. Experimental results denote that our proposed solution can yield 75% decrease in multi-join queries processing efforts and 74% decrease in the total amount of false-positive results in a faster manner and with better performance than previous methods.

Keywords

Query optimization Multi-join queries Encrypted database Bucket-based encryption Ant colony optimization 

References

  1. 1.
    Alamery, M., Faraahi, A., Javadi, H.H.S., Nourossana, S., Erfani, H.: Multi-join query optimization using the bees algorithm. In: Distributed Computing and Artificial Intelligence, pp. 449–457. Springer (2010)Google Scholar
  2. 2.
    Bellman, R.: Dynamic programming treatment of the travelling salesman problem. J. ACM (JACM) 9(1), 61–63 (1962)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Cordon, O., de Viana I.N.F., Herrera, F., Moreno, L.: A new ACO model integrating evolutionary computation concepts: the best-worst ant system. In: Proceedings of the 2nd International Workshop on Ant Algorithms- From Ant Colonies to Artificial Ants, pp 22–29 (2000)Google Scholar
  4. 4.
    Ding, W., Lv, X.: Database multi-joint query optimization based on generic-tabu algorithm. J. Converg. Inf. Technol. (JCIT) 7(16), 263–270 (2012)Google Scholar
  5. 5.
    Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)CrossRefGoogle Scholar
  6. 6.
    Flajolet, P., Sedgewick, R.: Analytic Combinatorics. Cambridge University Press, Cambridge, https://books.google.com.ua/books?id=0h-4QcA1c1QC (2009)
  7. 7.
    Golshanara, L., Rankoohi, S.M.T.R., Shah-Hosseini, H.: A multi-colony ant algorithm for optimizing join queries in distributed database systems. Knowl. Inf. Syst. 39(1), 175–206 (2014)CrossRefGoogle Scholar
  8. 8.
    Gonçalves, F.A.C.A., Guimarães, F.G., Souza, M.J.F.: Query join ordering optimization with evolutionary multi-agent systems. Exp. Syst. Appl. 41(15), 6934–6944 (2014)CrossRefGoogle Scholar
  9. 9.
    Hacigümüş, H., Iyer, B., Li, C., Mehrotra, S.: Executing SQL over eencrypted data in the database-service-provider model. In: Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data, pp. 216–227. ACM (2002)Google Scholar
  10. 10.
    Hacigümüş, H., Iyer, B., Mehrotra, S.: Query optimization in encrypted database systems. In: Database Systems for Advanced Applications, pp. 43–55. Springer (2005)Google Scholar
  11. 11.
    Hameurlain, A., Morvan, F.: Evolution of query optimization methods. In: Transactions on Large-Scale Data-and Knowledge-Centered Systems I, vol. 5740, pp. 211–242. Springer (2009)Google Scholar
  12. 12.
    Hore, B., Mehrotra, S., Tsudik, G.: A privacy-preserving index for range queries. In: Proceedings of the 30th International Conference on Very Large Databases, Vol. 30, pp. 720–731. VLDB Endowment (2004)Google Scholar
  13. 13.
    Ioannidis, K.B., Ferris, M.C.: A Genetic Algorithm for Database Query Optimization. Morgan Kaufmann Publishers, San Francisco (1991)Google Scholar
  14. 14.
    Ioannidis, Y.E., Wong, E.: Query optimization by simulated annealing. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, vol. 16, pp. 9–22. ACM (1987)Google Scholar
  15. 15.
    Kadkhodaei, H., Mahmoudi, F.: A combination method for join ordering problem in relational databases using genetic algorithm and ant colony. In: Proceedings of the IEEE International Conference on Granular Computing (GrC), pp. 312–317. IEEE (2011)Google Scholar
  16. 16.
    Li, N., Liu, Y., Dong, Y., Gu, J.: Application of ant colony optimization algorithm to multi-join query optimization. In: Advances in Computation and Intelligence, vol. 5370, pp. 189–197. Springer (2008)Google Scholar
  17. 17.
    Matysiak, M.: Efficient optimization of large join queries using tabu search. Inf. Sci. 83(1), 77–88 (1995)CrossRefGoogle Scholar
  18. 18.
    Montgomery, DC.: Design and Analysis of Experiments. Wiley, New York (2008)Google Scholar
  19. 19.
    Saedi AKZA, Deris, M.B.M., et al.: An efficient multi join query optimization for DBMS using swarm intelligent approach. In: Proceedings of the IEEE 4th World Congress on Information and Communication Technologies (WICT), pp. 113–117. IEEE (2014)Google Scholar
  20. 20.
    Selinger, P.G., Astrahan, M.M., Chamberlin, D.D., Lorie, R.A., Price, T.G.: Access path selection in a relational database management system. In: Proceedings of the 1979 ACM SIGMOD International Conference on Management of Data, pp. 23–34. ACM (1979)Google Scholar
  21. 21.
    Steinbrunn, M., Moerkotte, G., Kemper, A.: Heuristic and randomized optimization for the join ordering problem. VLDB J. Int. J. Very Large Data Bases 6(3), 191–208 (1997)CrossRefGoogle Scholar
  22. 22.
    Stützle, T., Hoos, H.H.: MAX-MIN ant system. Future Gener. Comput. Syst. 16(8), 889–914 (2000)CrossRefzbMATHGoogle Scholar
  23. 23.
    Tang, Y., Yun, J.: A method for reducing false hits in querying encrypted databases. In: Proceedings of the 8th IEEE International Conference on and Enterprise Computing, E-Commerce, and E-Services, p. 22. IEEE (2006)Google Scholar
  24. 24.
    Tang, Y., Zhang, L.: Adaptive bucket formation in encrypted databases. In: Proceedings of the IEEE Conference on e-Technology, e-Commerce and e-Service, pp. 116–119. IEEE (2005)Google Scholar
  25. 25.
    Tang, Y., Yun, J., Zhou, Q.: A multi-agent based method for reconstructing buckets in encrypted databases. In: Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT’06), pp. 564–570. IEEE (2006)Google Scholar
  26. 26.
    Tucker, A.B.: Computer Science Handbook, 2nd edn. CRC Press, Boca Raton (2004)Google Scholar
  27. 27.
    Vance, B., Maier, D.: Rapid bushy join-order optimization with cartesian products. ACM SIGMOD Record ACM 25, 35–46 (1996)CrossRefGoogle Scholar
  28. 28.
    Wong, E., Youssefi, K.: Decomposition—a strategy for query processing. ACM Trans. Database Syst. (TODS) 1(3), 223–241 (1976)CrossRefGoogle Scholar
  29. 29.
    Yang, X., Li, L., Ng, YK., Wang, B., Yu, G.: Associated load shedding strategies for computing multi-joins in sensor networks. In: Proceedings of the 11th International Conference on Database Systems for Advanced Applications (DASFAA 2006), vol. LNCS 3882, pp. 50–64. Springer (2006)Google Scholar
  30. 30.
    Zhou, Y., Wan, W., Liu, J.: Multi-joint query optimization of database based on the integration of best-worst ant algorithm and genetic algorithm. In: Proceedings of the IET International Communication Conference on Wireless Mobile and Computing (CCWMC 2009), pp. 543–546. IET (2009)Google Scholar
  31. 31.
    Zhou, Z.: Using heuristics and genetic algorithms for large scale database query optimization. J. Inf. Comput. Sci. 2(4), 261–280 (2007)Google Scholar
  32. 32.
    ZQL.: A Java SQL Parser. (2015) http://zql.sourceforge.net/, Accessed 10 Dec, 2015

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer EngineeringSharif University of TechnologyTehranIran

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