Skip to main content
SpringerLink
Log in
Book cover

International Conference of Pioneering Computer Scientists, Engineers and Educators

ICPCSEE 2017: Data Science pp 144–152Cite as

Research on XDR Bill Compression Under Big Data Technology

  • Conference paper
  • First Online:

  • 2481 Accesses

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 727))

Abstract

Communication industry has been walking in front of the big data technology application. Some studies on precision-controlled compression method are carried on in this paper by using the big data technology based on the widely used XDR bill in communication industry. According to different application scenarios, this paper puts forward targeted compression strategy and technological implementation method and verify the high efficiency of associated method in practice. It solves the problem of occupying large storage and low analysis efficiency in areas like the storage of the massive XDR bill, pretreatment, aggregation. It provides valuable references for telecommunication-related researchers and engineering practitioner in respect of using the big data technology.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Jiang, G., Hu, F., Shi, L.: Urban functional area identification based on call detail record data. J. Comput. Appl. 36(7), 2046–2050 (2016)

    Google Scholar 

  2. Li, B., Shen, L., Dai, P., Ren, X.: Methods of TD-LTE signaling data accuracy verification. Telecom Eng. Tech. Stand. (2), 22–27 (2017)

    Google Scholar 

  3. Xu, H., Xu, J.: Calculation of spatial position data based on mobile phone signaling work and live. Beijing Surv. Mapp. (6), 69–71 (2016)

    Google Scholar 

  4. Liu, G., Wang, X., Zhang, J., Li, S.: Study on intelligent management and control of tourist attraction based on mobile signaling data. J. Univ. Electron. Sci. Technol. China 44(5), 769–777 (2015)

    Google Scholar 

  5. Wu, S., Luo, J., Zhou, Y., Lin, J., Shu, Z.: Method of real-time traffic statistics using mobile network signaling. Appl. Res. Comput. 31(3), 776–779 (2014)

    Google Scholar 

  6. Du, C., Jiang, S.: Research on user ridership characteristic based on mobile signaling data. Mob. Commun. 39(23), 9–12 (2015)

    Google Scholar 

  7. Sui, Y., Shen, L., Tao, L., Dai, P., Wan, R., Wang, W.: Research on user location based on signaling big data. Telecommun. Sci. (s1), 197–201 (2016)

    Google Scholar 

  8. Li, X., Gong, Q.: Research on intelligent verification scheme of signaling XDR data quality. Shandong Commun. Technol. 36(4), 1–4 (2016)

    Google Scholar 

Download references

Acknowledgements

This work is supported by Heilongjiang Provincial Education Department Science and Technology Research Project (No. 12531492). Many thanks to the anonymous reviewers, whose insightful comments made this a better paper.

Author information

Authors and Affiliations

  1. Department of Electronic Engineering, Heilongjiang University, Harbin, Heilongjiang, China

    Bing Zhao & Sining Zhang

  2. Vixtel Technologies Holdings Limited, Beijing, China

    Jun Zheng

Authors
  1. Bing Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sining Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bing Zhao .

Editor information

Editors and Affiliations

  1. Central South University, Changsha, China

    Beiji Zou

  2. Central South University, Changsha, China

    Min Li

  3. Harbin Institute of Technology, Harbin, China

    Hongzhi Wang

  4. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  5. Harbin University of Science and Technology, Harbin, China

    Wei Xie

  6. Harbin Sea of Clouds and Computer Technology, Harbin, China

    Zeguang Lu

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Zhao, B., Zhang, S., Zheng, J. (2017). Research on XDR Bill Compression Under Big Data Technology. In: Zou, B., Li, M., Wang, H., Song, X., Xie, W., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 727. Springer, Singapore. https://doi.org/10.1007/978-981-10-6385-5_13

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-6385-5_13

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6384-8

  • Online ISBN: 978-981-10-6385-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation