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Fuzzy clustering-based skyline query preprocessing algorithm for large-scale flow data analysis

  • Yifu Zeng
  • Yantao Zhou
  • Xu Zhou
  • Fei Zheng
Article
First Online:

Abstract

In order to improve the effectiveness of the large-scale stream data skyline query preprocessing algorithm, a large-scale stream data skyline query preprocessing algorithm based on fuzzy clustering analysis (kdStreamSky) is proposed in the paper. Firstly, relevant concept of the large-scale stream data skyline query preprocessing algorithm was described; then, in order to solve the problem—curse of data dimensionality of the large-scale stream data skyline query preprocessing algorithm, the corresponding data subset was constructed and meanwhile MapReduce calculation model was combined to realize the mean value clustering center selection for the parallel mobile social network; meanwhile, in order to improve algorithm stability and facilitate the design of information forwarding scheme, a distributed hierarchical clustering method was adopted for the clustering analysis of individuals and the design of hierarchical forwarding scheme; and finally, the corresponding simulation experiment was implemented to verify algorithm effectiveness.

Keywords

MapReduce parallel calculation Hierarchical clustering Large-scale Stream data Skyline query 
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Notes

Acknowledgements

National Natural Science Foundation of China (Grant No. 61472126).

References

  1. 1.
    Turcan S, Rohle D, Goenka A et al (2016) IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype. Nature 483(7390):479–483CrossRefGoogle Scholar
  2. 2.
    Akoglu L, Tong H, Koutra D (2015) Graph based anomaly detection and description: a survey. Data Min Knowl Discov 29(3):626–688MathSciNetCrossRefGoogle Scholar
  3. 3.
    Doulkeridis C, Nørvåg K (2014) A survey of large-scale analytical query processing in MapReduce. VLDB J 23(3):355–380CrossRefGoogle Scholar
  4. 4.
    Mohammed MA, Ghani MKA, Arunkumar N, Hamed RI, Mostafa SA, Abdullah MK, Burhanuddin MA (2018) Decision support system for nasopharyngeal carcinoma discrimination from endoscopic images using artificial neural network. J Supercomput.  https://doi.org/10.1007/s11227-018-2495-2 Google Scholar
  5. 5.
    Mohammed MA, Ghani MKA, Arunkumar N, Hamed RI, Abdullah MK, Burhanuddin MA (2018) A real time computer aided object detection of nasopharyngeal carcinoma using genetic algorithm and artificial neural network based on Haar feature fear. Future Gener Comput Syst.  https://doi.org/10.1016/j.future.2018.07.022 Google Scholar
  6. 6.
    Al-Bashir A, Al-Abed M, Amari H, Al-Rousan F, Bashmaf O, Abdulhay E, Al Abdi R, Arunkumar N, Tapas Bapu BR, Al-Basheer A (2018) Computer-based Cobb angle measurement using deflection points in adolescence idiopathic scoliosis from radiographic images. Neural Comput Appl.  https://doi.org/10.1007/s00521-018-3614-y Google Scholar
  7. 7.
    Khanna A, Jain S, Aggarwal T, Arunkumar N, Gupta D, Julka A, Albuquerque V (2018) Optimized cuttlefish algorithm for diagnosis of Parkinson’s disease. Cogn Syst Res 52:36–48CrossRefGoogle Scholar
  8. 8.
    Hussein AF, Arunkumar N, Ramirez-Gonzalez G, Abdulhay E, Tavares JMR, de Albuquerque VHC (2018) A medical records managing and securing blockchain based system supported by a genetic algorithm and discrete wavelet transform. Cogn Syst Res 52:1–11.  https://doi.org/10.1016/j.cogsys.2018.05.004 CrossRefGoogle Scholar
  9. 9.
    Wei J, Meng F, Arunkumar N (2018) A personalized authoritative user-based recommendation for social tagging. Future Gener Comput Syst.  https://doi.org/10.1016/j.future.2018.03.048 Google Scholar
  10. 10.
    Ashokkumar P, Arunkumar N, Don S (2018) Intelligent optimal route recommendation among heterogeneous objects with keywords. Comput Electr Eng 68:526–535CrossRefGoogle Scholar
  11. 11.
    Hussein AF, Arunkumar N, Burbano-Fernandez M, Ramirez-Gonzalez G, Abdulhay E, de Albuquerque VHC (2018) An automated remote cloud-based heart rate variability monitoring system. IEEE Access.  https://doi.org/10.1109/access.2018.2831209 Google Scholar
  12. 12.
    Sarvaghad-Moghaddam M, Orouji AA, Ramezani Z, Elhoseny M, Farouk A, Arunkumar N (2018) Modelling the spice parameters of SOI MOSFET using a combinational algorithm. Clust Comput.  https://doi.org/10.1007/s10586-018-2289-6 Google Scholar
  13. 13.
    Elhoseny M, Ramírez-González G, Abu-Elnasr OM, Shawkat SA, Arunkumar N, Farouk A (2018) Secure medical data transmission model for IoT-based healthcare systems. IEEE Access.  https://doi.org/10.1109/ACCESS.2018.2817615 Google Scholar
  14. 14.
    Vardhana M, Arunkumar N, Abdulhay E, Ramirez-Gonzalez G (2018) Convolutional neural network for bio-medical image segmentation with hardware acceleration. Cogn Syst Res 50:10–14CrossRefGoogle Scholar
  15. 15.
    Tharwat A, Elhoseny M, Hassanien AE, Gabel T, Arunkumar N (2018) Intelligent Bézier curve-based path planning model using chaotic particle swarm optimization algorithm. Clust Comput.  https://doi.org/10.1007/s10586-018-2360-3 Google Scholar
  16. 16.
    El-Dahshan ESA, Mohsen HM, Revett K et al (2014) Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm. Expert Syst Appl 41(11):5526–5545CrossRefGoogle Scholar
  17. 17.
    Dobre C, Xhafa F (2014) Intelligent services for big data science. Future Gener Comput Syst 37(2):267–281CrossRefGoogle Scholar
  18. 18.
    Gedik B, Schneider S, Hirzel M et al (2014) Elastic scaling for data stream processing. IEEE Trans Parallel Distrib Syst 25(6):1447–1463CrossRefGoogle Scholar
  19. 19.
    Sujay RN, Deka PC (2014) Support vector machine applications in the field of hydrology: a review. Appl Soft Comput J 19(6):372–386Google Scholar
  20. 20.
    Lee J, Turner Y, Popa L et al (2014) Application-driven bandwidth guarantees in datacenters. In: ACM Conference on SIGCOMM. ACM, pp 467–478Google Scholar
  21. 21.
    Moshref M, Yu M, Govindan R et al (2014) DREAM: dynamic resource allocation for software-defined measurement. In: ACM Conference on SIGCOMM. ACM, pp 419–430Google Scholar
  22. 22.
    Han J, Zhou P, Zhang D et al (2014) Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding. ISPRS J Photogramm Remote Sens 89(1):37–48CrossRefGoogle Scholar

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

Authors and Affiliations

  1. 1.College of Electrical and Information EngineeringHunan UniversityChangshaChina
  2. 2.College of Computer Science and Electronic EngineeringHunan UniversityChangshaChina

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