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FreshJoin: An Efficient and Adaptive Algorithm for Set Containment Join

  • Jizhou LuoEmail author
  • Wei Zhang
  • Shengfei Shi
  • Hong Gao
  • Jianzhong Li
  • Tao Zhang
  • Zening Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11642)

Abstract

This paper revisits set containment join (SCJ), which has many fundamental applications in commercial and scientific fields. To improve the performance further, this paper proposes a new adaptive parameter-free in-memory algorithm for SCJ, named as \(\mathsf {FreshJoin}\). It accomplishes this by exploiting two flat indices, which record three kinds of signatures (i.e.,  the two least frequent elements and a hash signature). Experiments on 16 real-life datasets show that \(\mathsf {FreshJoin}\) usually reduces more than 50% of space costs while remains as competitive as the state-of-the-art algorithms in running time.

Keywords

Set containment join Frequency hash Join algorithm 

References

  1. 1.
    Yang, J., Zhang, W., Yang, S., Zhang, Y., Lin, X.: TT-join: efficient set containment join. In: Proceedings of ICDE 2017, pp. 509–520 (2017)Google Scholar
  2. 2.
    Kunkel, A., Rheinländer, A., Schiefer, C., Helmer, S., Bouros, P., Leser, U.: Piejoin: towards parallel set containment joins. In: Baumann, P., Manolescu-Goujot, I., Trani, L. (eds.) SSDBM 2016, pp. 11–22 (2016)Google Scholar
  3. 3.
    Luo, Y., Fletcher, G., Hidders, J., De Bra, P.: Efficient and scalable trie-based algorithms for computing set containment relations. In: Gehrke, J., Lehner, W., Shim, K., et al. (eds.) ICDE 2015, pp. 303–314 (2015)Google Scholar
  4. 4.
    Bouros, P., Mamoulis, N., Ge, S., Terrovitis, M.: Set containment join revisited. Knowl. Inf. Syst. 49, 1–28 (2015)CrossRefGoogle Scholar
  5. 5.
    Jampani, R., Pudi, V.: Using prefix-trees for efficiently computing set joins. In: Zhou, L., Ooi, B.C., Meng, X. (eds.) DASFAA 2005. LNCS, vol. 3453, pp. 761–772. Springer, Heidelberg (2005).  https://doi.org/10.1007/11408079_69CrossRefGoogle Scholar
  6. 6.
    Mamoulis, N.: Efficient processing of joins on set-valued attributes. In: Halevy, A., Ives, Z., Doan, A. (eds.) SIGMOD 2003, pp. 157–168 (2003)Google Scholar
  7. 7.
    Melnik, S., Molina, H.: Adaptive algorithms for set containment joins. ACM Trans. Database Syst. 28(1), 56–99 (2003)CrossRefGoogle Scholar
  8. 8.
    Melnik, S., Garcia-Molina, H.: Divide-and-conquer algorithm for computing set containment joins. In: Jensen, C.S., et al. (eds.) EDBT 2002. LNCS, vol. 2287, pp. 427–444. Springer, Heidelberg (2002).  https://doi.org/10.1007/3-540-45876-X_28CrossRefGoogle Scholar
  9. 9.
    Ramasamy, K., Patel, J., Naughton, J., Kaushik, R.: Set containment joins: the good, the bad and the ugly. In: Abbadi, A., Brodie, M., Chakravarthy, S., et al. (eds.) VLDB 2000, pp. 386–395 (2000)Google Scholar
  10. 10.
    Helmer, S., Moerkotte, G.: Evaluation of main memory join algorithms for joins with set comparison predicates. In: Jarke, M., Carey, J., Dittrich, R., et al. (eds.) VLDB 1997, pp. 386–395 (1997)Google Scholar
  11. 11.
    Zhu, E., Nargesian, F., Pu, K., Miller, R.: LSH ensemble: internet scale domain search. Proc. VLDB Endow. 9(12), 1185–1196 (2016)CrossRefGoogle Scholar
  12. 12.
    Mann, W., Augsten, N., Bouros, P.: An empirical evaluation of set similarity join techniques. Proc. VLDB Endow. 9(9), 636–647 (2016)CrossRefGoogle Scholar
  13. 13.
    Luo, J., Gao, H., Li, J., et al.: Techique report on Freshjoin an adaptive algorithm for set containment join.  https://doi.org/10.13140/RG.2.2.32373.63207

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jizhou Luo
    • 1
    Email author
  • Wei Zhang
    • 1
  • Shengfei Shi
    • 1
  • Hong Gao
    • 1
  • Jianzhong Li
    • 1
  • Tao Zhang
    • 1
  • Zening Zhou
    • 1
  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHeilongjiangChina

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