体育赛事投注记录

体育赛事投注记录advertisement

An Indexed Non-probability Skyline Query Processing Framework for Uncertain Data

  • Ma’aruf Mohammed LawalEmail author
  • Hamidah Ibrahim
  • Nor Fazlida Mohd Sani
  • Razali Yaakob
Conference paper
  • 60 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1141)

Abstract

Today, we are in the era of making multi-criteria decisions based on analysis of available data collected from autonomous databases that usually contain uncertain data. Skyline query technique returns a set of interesting objects (skylines) to the user by eliminating objects that are dominated by other objects within the database. Obviously, without doubt streamlining the process of processing skylines in providing answers to user-specified queries is inevitable. In this paper, we proposed SQUiD framework that combines an index-based technique with a prediction method to reduce the computational time for computing skylines over uncertain high-dimensional data. Through experimentations, results obtained clearly demonstrate the superiority of SQUiD framework over SkyQUD framework and BBIS体育赛事投注记录 algorithm.

Keywords

Skyline query Non-probability approach Indexing technique Uncertain data 

References

  1. 1.
    Lawal, M.M., Ibrahim, H., Mohd Sani, F., Yaakob, R.: Skyline query algorithms for computing uncertain databases: a survey, a chapter in a book: The landscape of Computing and Informatics Research Edition (2016–2017), pp. 173-191 (2018)
  2. 2.
    Börzsönyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: Proceedings of the International Conference on Data Engineering, pp. 421–430 (2001).  
  3. 3.
    Gothwal, H., Choudhary, J., Singh, D.P.: The survey on skyline query processing for data-specific applications. In: Proceedings of the 3rd International Conference on Internet of Things and Connected Technologies (ICIOTCT), pp. 26–27 (2018).  
  4. 4.
    Saad, N.H.M., Ibrahim, H., Alwan, A.A., Sidi, F., Yakoob, R.: A framework for evaluating skyline query on uncertain autonomous database. Proceedings of the International Conference on Computational Sciences, pp. 1546–1556 (2014).  
  5. 5.
    Saad, N.H.M., Ibrahim, H., Alwan, A.A., Sidi, F., Yakoob, R.: Computing range skyline query on uncertain dimension. In: Hartmann, S., Ma, H. (eds.) Lecture Note in Computer Science: vol. 9828, pp. 377–388. Springer-Verlag. Database and Expert System Applications, (2016).  
  6. 6.
    Saad, N.H.M., Ibrahim, H., Sidi, F., Yaakob, R.: Skyline probabilities with range query on uncertain dimensions. Proceedings of the Advances in Computer Communication and Computational Sciences, pp. 225–242 (2019).  
  7. 7.
    Khalefa, M.E., Mokbel, M.F., Levandoski, J.J.: Skyline query processing for uncertain data. Proceedings of the International Conference on Information and Knowledge Management, pp. 1293–1296 (2010).  
  8. 8.
    Li, X., Wang, Y., Li, X., Wang, G.: Skyline query processing on interval uncertain data. Proceedings of the 15th International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops, pp. 87–92 (2012).  
  9. 9.
    Pei, J., Jiang, B., Lin, X., Yuan, Y.: Probabilistic skyline on uncertain data. Proceedings of the International Conference on Very Large Database, pp. 15–26 (2007)
  10. 10.
    Zadeh, L.A.: On fuzzy algorithms. Proceedings of the International on Fuzzy sets, Fuzzy logic, and Fuzzy systems, pp. 127–147 (1996).  
  11. 11.
    Han, H., Li, J., Yang, D., Wang, J.: Efficient Skyline Computation on Big Data. IEEE J. Trans. Knowl. Data Eng. 25(11), 2521–2535 (2013).  
  12. 12.
    Papadias, D., Tao, Y., Fu, G., Seeger, B.: Progressive skyline computation in database systems. Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 41–82 (2003).  
  13. 13.
    Swidan, M.B., Alwan, A.A., Turaev, S., Gulzar, Y.: A Model for Processing Skyline Queries in Crowd-sourced Databases. Indones. J. Electr. Eng. Comput. Sci. 10(2), 798–806 (2018).  
  14. 14.
    Tan, K.L., Eng, P.K., Ooi, B.C.: Efficient progressive skyline computation. Proceedings of the International Conference on Very Large Database, pp. 301–310 (2001)
  15. 15.
    Kossmann, D., Ramsak, F., Rost, S.: Shooting stars in the sky: an online algorithm for skyline queries. Proceedings of the International Conference on Very Large Database, pp. 275–286 (2002)
  16. 16.
    Tóth-Laufer, E., Takács, M.: The effect of aggregation and defuzzification method selection on the risk level calculation. Proceedings of the 2012 IEEE 10th International Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 131–136 (2012).  

Copyright information

© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  • Ma’aruf Mohammed Lawal
    • 1
    • 2
    Email author
  • Hamidah Ibrahim
    • 2
  • Nor Fazlida Mohd Sani
    • 2
  • Razali Yaakob
    • 2
  1. 1.Department of Computer Science, Faculty of Physical SciencesAhmadu Bello UniversityZariaNigeria
  2. 2.Department of Computer Science, Faculty of Computer Science and Information TechnologyUniversiti Putra MalaysiaSerdangMalaysia

Personalised recommendations