Skip to main content

Advertisement

Springer Nature Link
Log in

RETRACTED ARTICLE: A QoS optimization system for complex data cross-domain request based on neural blockchain structure

  • IAPR-MedPRAI
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

This article was retracted on 12 December 2022

This article has been updated

Abstract

Various computing environments are constantly emerging which are using different technology platforms and security mechanisms from each other. The development of network technology makes cross-domain access between various systems necessary, which requires seamless information sharing and data exchange between systems, thus eliminating the phenomenon of information islands. With its unique consensus mechanism and compatible encryption algorithms, it has gradually attracted attention in various fields. Many people believe that blockchain technology is a revolution in Internet technology in the future, which is a huge innovation in information infrastructure technology as well. In order to ensure a high QoS of the data grid, the system needs to overcome many unstable factors of the network and the grid nodes. Resource (capability) reservation, copy deployment, buffer mechanism, parallel data transfer, and data storage and recovery are the main means to solve such problems. Above all, this paper proposed a QoS optimization system for complex data cross-domain request based on neural blockchain structure. Experimental results show that the proposed method has higher robustness and efficiency.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Change history

References

  1. Wang Qingbo, Jin Xin, He Le et al (2009) Virtualization and cloud computing. Publishing House of Electronics Industry, Beijing, pp 1–25

    Google Scholar 

  2. National Institute of Standards and Technology homepage. http://www.nist.gov/

  3. James HA, Hawick KA (2005) Scientific data management in a grid environment. J Grid Comput 3:39–51

    Article  Google Scholar 

  4. Vazhkudai S, Schopf JM (2003) Using regression techniques to predict large data transfers. Int J High-Perform Comput Appl 17(3):249–260

    Article  Google Scholar 

  5. Li J (2007) Research on resource management related strategies and algorithms of data grid. PhD thesis of Chongqing University, pp 1–5

  6. Zhou D (2004) Key theory and technology research of service grid with QoS Guarantee. Doctoral Dissertation of Huazhong University of Science and Technology, pp 29–30

  7. Kouzes RT, Anderson GA, Elbert ST et al (2009) The changing paradigm of data-intensive computing. IEEE Comput 42(1):26–34

    Article  Google Scholar 

  8. Jun Xu, Liu Quanbo, Li Yushun et al (2010) Service-oriented grid computing: a new distributed computing system and middleware. Science Press, Beijing

    Google Scholar 

  9. Brandic I, Pllana S, Benkner S (2008) Specification, planning, and execution of QoS-aware Grid workflows within the Amadeus environment. Concurr Comput 20:331–345

    Article  Google Scholar 

  10. Ali R, Barrdear J, Clews R et al (2014) The economics of digital currencies. Soc Sci Electron Publish 54:276–286

    Google Scholar 

  11. Fujiki H, Tanaka M (2014) Currency demand, new technology, and the adoption of electronic money: micro evidence from Japan. Econ Lett 125(1):5–8

    Article  MathSciNet  Google Scholar 

  12. Qiu Y, Bi Y, Li Y, Wang H (2018) High resolution remote sensing image denoising algorithm based on sparse representation and adaptive dictionary learning. In: Computational vision and bio inspired computing. Springer, Cham, pp 892–901

  13. Wang Xiaogang (2015) Deep learning in image recognition. Commun CCF 11(8):15–23

    Google Scholar 

  14. Sui Z (2017) Hierarchical text topic modeling with applications in social media-enabled cyber maintenance decision analysis and quality hypothesis generation (Doctoral dissertation, The Ohio State University)

  15. Parker N, Allen TT, Sui Z (2017) K-means subject matter expert refined topic model methodology (No. TRAC-M-TR-17-008). TRAC-Monterey Monterey United States

  16. Allen TT, Sui Z, Parker NL (2017) Timely decision analysis enabled by efficient social media modeling. Decis Anal 14(4):250–260

    Article  MathSciNet  MATH  Google Scholar 

  17. Brogi C, Huisman JA, Weihermüller L, Pätzold S, von Hebel C, van der Kruk J, Vereecken H (2018) Large-scale subsurface characterization using image classification of multi-configuration electromagnetic induction data assisted by direct soil sampling. In EGU general assembly conference abstracts, vol 20, p 8249

  18. Aljawarneh SA, Moftah RA, Maatuk AM (2016) Investigations of automatic methods for detecting the polymorphic worms signatures. Future Gener Comput Syst 60:67–77

    Article  Google Scholar 

  19. Aljawarneh SA, Alawneh A, Jaradat R (2017) Cloud security engineering: early stages of SDLC. Future Gener Comput Syst 74:385–392

    Article  Google Scholar 

  20. Aljawarneh SA, Vangipuram R, Puligadda VK, Vinjamuri J (2017) G-SPAMINE: an approach to discover temporal association patterns and trends in internet of things. Future Gener Comput Syst 74:430–443

    Article  Google Scholar 

  21. Aljawarneh S (2011) A web engineering security methodology for e-learning systems. Netw Secur 2011(3):12–15

    Article  Google Scholar 

  22. Aljawarneh S, Yassein MB (2017) A resource-efficient encryption algorithm for multimedia big data. Multimed Tools Appl 76(21):22703–22724

    Article  Google Scholar 

  23. Aljawarneh S, Aldwairi M, Yassein MB (2018) Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. J Comput Sci 25:152–160

    Article  Google Scholar 

  24. Hossain MS, Xu C, Li Y, Bilbao J, El Saddik A (2018) Advances in next-generation networking technologies for smart healthcare. IEEE Commun Mag 56(4):14–15

    Article  Google Scholar 

Download references

Acknowledgement

The research is supported by the: 1. Project funded by National Key R&D Program of China: International cooperation between governments in scientific and technological innovation (No.YS2017YFGH002008): Horizon 2020 Urban Inclusive and Innovative Nature; 2. Project funded by China Postdoctoral Science Foundation: Assessment and optimization of urban lifeline resilience based on the big data; 3. Project funded by the Project of Macau Foundation: Social mutual aid (disaster relief) application.

Author information

Authors and Affiliations

  1. The Post-Doctoral Research Center, Zhuhai Da Hengqin Science and Technology Development Co., Ltd, Hengqin, China

    Lianbing Deng, Daming Li & Xiang Yao

  2. Huazhong University of Science and Technology, Wuhan, China

    Lianbing Deng

  3. Macau Big Data Research Centre for Urban Governance, City University of Macau, Macau, China

    Daming Li & Zhiming Cai

Authors
  1. Lianbing Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daming Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhiming Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiang Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daming Li.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s00521-022-08157-6

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deng, L., Li, D., Cai, Z. et al. RETRACTED ARTICLE: A QoS optimization system for complex data cross-domain request based on neural blockchain structure. Neural Comput & Applic 32, 16455–16469 (2020). https://doi.org/10.1007/s00521-019-04062-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04062-7

Keywords

Search

Navigation

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy