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Processing Unstructured Databases Using a Quantum Approach

  • H. AmellalEmail author
  • A. Meslouhi
  • A. El Allati
Conference paper
Part of the Lecture Notes in Intelligent Transportation and Infrastructure book series (LNITI)

Abstract

One of the most fundamental choices to store the big data it’s the use of unstructured databases. However, the classical algorithms used in NoSQL databases suffer from slow execution of orders, especially in search operations. In order to decrease the data time processing in general and more particularly the search period in unstructured databases, we suggest in this work the use of a quantum approach based on Grover’s algorithm.

Keywords

Data mining Unstructured databases Relational databases Quantum algorithms 

References

  1. 1.
    Zadrozny, P., Kodali, R.: Big data analytics using Splunk: deriving operational intelligence from social media, machine data, existing data warehouses, and other real-time streaming sources (2013). ISBN: 143025761X, 9781430257615Google Scholar
  2. 2.
    Grover, L.: Fast quantum mechanical algorithm for database search. In: Proceedings of the 28th Annual ACM Symposium on Theory of Computing (STOC_96), pp. 212–219 (1996)Google Scholar
  3. 3.
    Grover: Quantum mechanics helps in searching for a needle in a haystack. Phys. Rev. Lett. 79(2), 325–328 (1997)CrossRefGoogle Scholar
  4. 4.
    Grof, J., Weinberg, P.: SQL the Complete Reference, 3rd edn. McGraw-Hill Inc., New York (2010)Google Scholar
  5. 5.
    Cur, O., Blin, G.: RDF Database Systems: Triples Storage and SPARQL Query Processing, 1st edn. Morgan Kaufmann Publishers Inc., San Francisco (2014)Google Scholar
  6. 6.
    Gulutzan, P., Pelzer, T.: SQL Performance Turning. Addison-Wesley Longman Publishing Co., Inc., Boston (2002)Google Scholar
  7. 7.
    Wood, P.T.: Query languages for graph databases. SIGMOD Rec. 41(1), 50–60 (2012)CrossRefGoogle Scholar
  8. 8.
    Ohlhorst, F.J.: Big Data Analytics: Turning Big Data Into Big Money, p. 21. Wiley, New York (2012)CrossRefGoogle Scholar
  9. 9.
    Demchenko, Y., Zhao, Z., Grosso, P., Wibisono, A., De Laat, C.: Addressing big data challenges for scientific data infrastructure. In: 2012 IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom), pp. 614–617 (2012)Google Scholar
  10. 10.
    John, Walker S.: Big Data: A Revolution That Will Transform How We Live, Work, and Think. Taylor & Francis, New York (2014)Google Scholar
  11. 11.
    Dean, J., Ghemawat, S.: MapReduce: simplifed data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  12. 12.
    Riondato, M., DeBrabant, J.A., Fonseca, R., Upfal, E.: PARMA: a parallel randomized algorithm for approximate association rules mining in MapReduce. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 85–94. ACM, New York (2012)Google Scholar
  13. 13.
    Oruganti, S., Ding, Q., Tabrizi, N.: Exploring Hadoop as a platform for distributed association rule mining. In: Future Computing 2013 the fifth International Conference on Future Computational Technologies and Applications, pp. 62–67 (2013)Google Scholar
  14. 14.
    Kovacs, F., Illés, J.: Frequent itemset mining on hadoop. In: 2013 IEEE 9th International Conference on Computational Cybernetics (ICCC), pp. 241–245. IEEE, New York (2013)Google Scholar
  15. 15.
    White, T.: Hadoop: The Definitive Guide. O’Reilly Media Inc., Farnham (2009)Google Scholar
  16. 16.
    Khan, M., Jin, Y., Li, M., Xiang, Y., Jiang, C.: Hadoop performance modeling for job estimation and resource provisioning. IEEE Trans. Parallel Distrib. Syst. 27(2), 441–454 (2016).  https://doi.org/10.1109/TPDS.2015.2405552CrossRefGoogle Scholar
  17. 17.
    Hadoop, A.: Welcome to Apache Hadoop. http://hadoop.apache.org/. Accessed 10 Mar 2017
  18. 18.
    Plimpton, S.J., Devine, K.D.: Mapreduce in mpi for large-scale graph algorithms. Parallel Comput. 37(9), 610–632 (2011)CrossRefGoogle Scholar
  19. 19.
    Kollmitzer, C., Pivk, M.: Applied Quantum Cryptogtraphy, Lecture Notes in Physics, vol. 797. Springer (2010), ISBN 978-3-642-04829-6Google Scholar
  20. 20.
    McMahon, D.: Quantum computing explained. Wile Interscience A John Wiley Sons, Inc., Publication, Computer society IEEE (2007)Google Scholar
  21. 21.
    Kaye, P., Laflamme, R., Mosca, M.: An Introduction to Quantum Computing. Oxford University Press, Oxford (2007)zbMATHGoogle Scholar
  22. 22.
    Imre, S., Balazs, F.: Quantum Computing and Communications an Engineering Approach. Wiley (2005)Google Scholar
  23. 23.
    Aharonov, D.: Quantum computation a review. Annual Review of Computational Physics VI, pp. 259–346. World Scientific (1998)Google Scholar
  24. 24.
    Ambainis, A.: Quantum walk algorithm for element distinctness. SIAM J. Comput. 37, 210239 (2007)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Dirac, P.A.M.: The Principles of Quantum Mechanics, 3rd edn. Clarendon Press, Oxford (1947)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University Mohammed V Faculty of SciencesRabatMorocco
  2. 2.Laboratory of Engineering Sciences, Faculty of Sciences and TechniquesAjdir, Al-HoceimaMorocco

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