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M-N Hashing: Search Time Optimization with Collision Resolution Using Balanced Tree

  • Arushi Agarwal
  • Sashakt PathakEmail author
  • Sakshi Agarwal
Conference paper
  • 160 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1206)

Abstract

In the field of networking, storing and fast lookup from the large amount of data are two important measures. Hashing is one of the well-known techniques for indexing and retrieving data from the database efficiently. It is mostly used for fast lookups in the fields which require quick results from the database. As the size of the database increases, the number of collisions in the hash table also increases which handled through collision resolution techniques. Traditional algorithms like Separate Chaining, Linear Probing, and Quadratic probing takes linear search time. The tremendous increase of data in recent years requires more profound hash table implementation. In this paper, we have proposed a new and innovative way of implementing hash table to handle collisions more efficiently in logarithmic time i.e. M-N Hashing. To handle the collisions with a scalable sized hash table, the proposed algorithm used the concept of the AVL tree. The performance of the proposed algorithm is analyzed using continuous integer dataset that varies from 0 to 100000. Through experiments, it is depicted that M-N hashing improved the search time up to 99.97% with respect to contemporary algorithms i.e. Separate Chaining, Linear Probing, and Quadratic probing.

Keywords

Hashing Separate Chaining Linear Probing Quadratic Probing Search time complexity 

References

  1. 1.
    Mun, H.-J., Hong, S., Shin, J.: A novel secure and efficient hash function with extra padding against rainbow table attacks. Cluster Comput. 21(1), 1161–1173 (2018)CrossRefGoogle Scholar
  2. 2.
    Sapuntzakis, C.P., Chandra, R., Pfaff, B., Chow, J., Lam, M.S., Rosenblum, M.: Optimizing the migration of virtual computers. In: 5th Symposium on Operating Systems Design and Implementation, pp. 377–390 (2002)Google Scholar
  3. 3.
    Broder, A., Mitzenmacher, M.: Using multiple hash functions to improve IP lookups. In: IEEE, INFOCOM (2001), pp. 1454–1463Google Scholar
  4. 4.
    Nimbe, P., Frimpong, S.O., Opoku, M.: An efficient strategy for collision resolution in hash tables. Int. J. Comput. Appl. 99(10), 35–41 (2014)Google Scholar
  5. 5.
    Askitis, N., Zobel, J.: Cache-conscious collision resolution in string hash tables. In: Consens, M., Navarro, G. (eds.) SPIRE 2005. LNCS, vol. 3772, pp. 91–102. Springer, Heidelberg (2005).  https://doi.org/10.1007/11575832_11CrossRefGoogle Scholar
  6. 6.
    Liu, D., Xu, S.: Comparison of hash table performance with open addressing and closed addressing: an empirical study. Int. J. Network. Distrib. Comput. 3, 60–68 (2015)CrossRefGoogle Scholar
  7. 7.
    Bello, S.A., Liman, A.M., Gezawa, A.S., Garba, A., Ado, A.: Comparative Analysis of Linear Probing, Quadratic Probing and Double Hashing techniques for resolving collision in a Hash table. Int. J. Sci. Eng. Res. 685–687 (2014) Google Scholar
  8. 8.
    Agarwal, A., Bhyravarapu, S., Krishna Chaitanya, N.V.: Matrix Hashing with two level of collision resolution. In: IEEE, pp. 526–532 (2018)Google Scholar
  9. 9.
    Dhar, S., Pandey, K., Premalatha, M., Suganya, G.: A tree based approach to improve traditional collision avoidance mechanisms of hashing. In: International Conference on Inventive Computing and Informatics (2017)Google Scholar
  10. 10.
    Fredman, M.L., Saks, M.E.: The cell probe complexity of dynamic data structures. In: STOC 1989 Proceedings of the Twenty-First Annual ACM Symposium on Theory of Computing, pp. 345–354 (1989)Google Scholar
  11. 11.
    Miller, J.L.: Routing cache for distributed hash tables. U.S. Patent No. 7,808,971. 5 October 2010Google Scholar
  12. 12.
    Pietrzyk, J., Ungethüm, A., Habich, D., Lehner, W.: Fighting the duplicates in hashing: conflict detection-aware vectorization of linear probing. In: Grust, T., et al. (eds.), BTW 2019. Gesellschaft für Informatik, Bonn (2019)Google Scholar
  13. 13.
    Tian, H., et al.: Dynamic- hash-table based public auditing for secure cloud storage. IEEE Trans. Serv. Comput. 10(5), 701–714 (2017)CrossRefGoogle Scholar
  14. 14.
    Tumblin, R., Ahrens, P., Hartse, S., Robey, R.W.: Parallel compact hash algorithms for computational meshes. SIAM J. Sci. Comput. 37(1), C31–C53 (2015)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Zhou, Y., Gao, C.: Research and improvement of a multi-pattern matching algorithm based on double hash. In: IEEE (2017)Google Scholar
  16. 16.
    Köppl, D.: Separate chaining meets compact hashing. arXiv preprint arXiv:1905.00163 (2019)
  17. 17.
    Maier, T., Sanders, P., Dementiev, R.: Concurrent hash tables: fast and general (?)! ACM Trans. Parallel Comput. (TOPC) 5(4), 16 (2019)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Arushi Agarwal
    • 1
  • Sashakt Pathak
    • 1
    Email author
  • Sakshi Agarwal
    • 1
  1. 1.Jaypee Institute of Information TechnologyNoidaIndia

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