Skip to main content
SpringerLink
Log in

RETRACTED ARTICLE: A data layout method suitable for workflow in a cloud computing environment with speech applications

  • Published:
International Journal of Speech Technology Aims and scope Submit manuscript

This article was retracted on 13 October 2022

This article has been updated

Abstract

A data layout method suitable for workflow in a cloud computing environment with speech applications is designed and implemented in this research. With the continuous advancement of cloud computing, Internet of Things, wireless communications, mobile Internet and other technologies, data is growing and accumulating at an unprecedented rate. This paper provides the novel efficient speech data processing layout considering prior information and the data structure. On the basis of in-depth research on existing data layout strategies, this paper summarizes several aspects such as incompatibility with energy-saving modes based on storage area division, incompatibility with the heterogeneous HDFS clusters, inflexible data block segmentation algorithms, and randomness of general storage node selection aspects. Especially, considering that speech data related lower-case read-ahead and basic read-ahead are quite different, reflected in the purpose of read-ahead, read-ahead objects, read-ahead execution level, read-ahead trigger conditions, read-ahead feasibility, we propose the efficient robust data mining model to capture the data structure and feature. Through comparing with the other state-of-the-art models, the experiment section gives the clear analysis on the performance.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

Change history

References

  • Abdel-Basset, M., Mohamed, M., & Chang, V. (2018). NMCDA: A framework for evaluating cloud computing services. Future Generation Computer Systems, 86, 12–29.

    Article  Google Scholar 

  • Crisp, S., & Dawdy, K. (2018). Building a magnetic resonance imaging safety culture from the ground up. Journal of Medical Imaging and Radiation Sciences, 49(1), 18–22.

    Article  Google Scholar 

  • Fan, E., Xie, W., Pei, J., Hu, K., & Li, X. (2018). Neutrosophic hough transform-based track initiation method for multiple target tracking. IEEE Access, 6, 16068–16080.

    Article  Google Scholar 

  • Huang, C., Lin, W., Lai, C., Li, X., Jin, Y., & Yong, Q. (2019a). Coupling the post-extraction process to remove residual lignin and alter the recalcitrant structures for improving the enzymatic digestibility of acid-pretreated bamboo residues. Bioresource technology, 285, 121355.

    Article  Google Scholar 

  • Huang, C., Wang, X., Liang, C., Jiang, X., Yang, G., Xu, J., et al. (2019b). A sustainable process for procuring biologically active fractions of high-purity xylooligosaccharides and water-soluble lignin from Moso bamboo prehydrolyzate. Biotechnology for Biofuels, 12(1), 189.

    Article  Google Scholar 

  • Jiao, J., Venkat, K., & Weissman, T. (2018a). Mutual information, relative entropy and estimation error in semi-martingale channels. IEEE Transactions on Information Theory, 64(10), 6662–6671.

    Article  MathSciNet  Google Scholar 

  • Jiao, Z., Gao, X., Wang, Y., Li, J., & Xu, H. (2018b). Deep Convolutional Neural Networks for mental load classification based on EEG data. Pattern Recognition, 76, 582–595.

    Article  Google Scholar 

  • Li, H., Lu, R., Misic, J., & Mahmoud, M. (2018c). Security and privacy of connected vehicular cloud computing. IEEE Network, 32(3), 4–6.

    Article  Google Scholar 

  • Li, J., Zhang, Y., Chen, X., & Xiang, Y. (2018b). Secure attribute-based data sharing for resource-limited users in cloud computing. Computers & Security, 72, 1–12.

    Article  Google Scholar 

  • Li, P., Li, J., Huang, Z., Gao, C. Z., Chen, W. B., & Chen, K. (2018a). Privacy-preserving outsourced classification in cloud computing. Cluster Computing, 21(1), 277–286.

    Article  Google Scholar 

  • Lu, Z. H., Zhao, W. L., Ji, L. B., Wen, R., Zhang, Y. S., Wu, J. F., et al. (2018). The histogram analysis of apparent diffusion coefficient maps with standard-or ultrahigh-b value diffusion-weighted MR imaging for differentiating the gleason grade of prostate cancer. Journal of Medical Imaging and Health Informatics, 8(3), 577–582.

    Article  Google Scholar 

  • Muhammad, K., Hamza, R., Ahmad, J., Lloret, J., Wang, H., & Baik, S. W. (2018). Secure surveillance framework for IoT systems using probabilistic image encryption. IEEE Transactions on Industrial Informatics, 14(8), 3679–3689.

    Article  Google Scholar 

  • Noor, T. H., Zeadally, S., Alfazi, A., & Sheng, Q. Z. (2018). Mobile cloud computing: Challenges and future research directions. Journal of Network and Computer Applications, 115, 70–85.

    Article  Google Scholar 

  • Pan, C. T., Chang, W. H., Kumar, A., Singh, S. P., Kaushik, A. C., Sharma, J., et al. (2019). Nanoparticles-mediated brain imaging and disease prognosis by conventional as well as modern modal imaging techniques: A comparison. Current Pharmaceutical Design, 25(24), 2637–2649.

    Article  Google Scholar 

  • Pavlichin, D. S., Jiao, J., & Weissman, T. (2019). Approximate profile maximum likelihood. Journal of Machine Learning Research, 20(122), 1–55.

    MathSciNet  MATH  Google Scholar 

  • Plageras, A. P., Psannis, K. E., Stergiou, C., Wang, H., & Gupta, B. B. (2018). Efficient IoT-based sensor BIG Data collection–processing and analysis in smart buildings. Future Generation Computer Systems, 82, 349–357.

    Article  Google Scholar 

  • Que, S., Awuah-Offei, K., Demirel, A., Wang, L., Demirel, N., & Chen, Y. (2019). Comparative study of factors affecting public acceptance of mining projects: Evidence from USA, China and Turkey. Journal of Cleaner Production, 237, 117634.

    Article  Google Scholar 

  • Shah-Mansouri, H., & Wong, V. W. (2018). Hierarchical fog-cloud computing for IoT systems: A computation offloading game. IEEE Internet of Things Journal, 5(4), 3246–3257.

    Article  Google Scholar 

  • Shao, C., Zhang, Q., Song, Y., & Zhu, D. (2020). Smart home healthcare system based on middleware and counter neural network. Journal of Medical Imaging and Health Informatics, 10(5), 1105–1112.

    Article  Google Scholar 

  • Thakur, S., Singh, A. K., Ghrera, S. P., & Elhoseny, M. (2019). Multi-layer security of medical data through watermarking and chaotic encryption for tele-health applications. Multimedia Tools and Applications, 78(3), 3457–3470.

    Article  Google Scholar 

  • Ting, D. S., Liu, Y., Burlina, P., Xu, X., Bressler, N. M., & Wong, T. Y. (2018). AI for medical imaging goes deep. Nature Medicine, 24(5), 539.

    Article  Google Scholar 

  • Wang, N., Gao, X., Tao, D., Yang, H., & Li, X. (2018). Facial feature point detection: A comprehensive survey. Neurocomputing, 275, 50–65.

    Article  Google Scholar 

  • Wang, Y., Li, J., & Wang, H. H. (2019). Cluster and cloud computing framework for scientific metrology in flow control. Cluster Computing, 22(1), 1189–1198.

    Article  Google Scholar 

  • Wang, S., Zhou, J., Liu, J. K., Yu, J., Chen, J., & Xie, W. (2016). An efficient file hierarchy attribute-based encryption scheme in cloud computing. IEEE Transactions on Information Forensics and Security, 11(6), 1265–1277.

    Article  Google Scholar 

  • Zhang, H., Yu, Y., Jiao, J., Xing, E.P., Ghaoui, L.E., Jordan, M.I. (2019). Theoretically principled trade-off between robustness and accuracy. arXiv preprint arXiv:1901.08573.

  • Zhang, S., Wang, H., & Huang, W. (2020). Palmprint identification combining hierarchical multi-scale complete LBP and weighted SRC. Soft Computing, 24(6), 4041–4053.

    Article  Google Scholar 

  • Zhang, S., Wang, H., Huang, W., & You, Z. (2018). Plant diseased leaf segmentation and recognition by fusion of superpixel, K-means and PHOG. Optik, 157, 866–872.

    Article  Google Scholar 

  • Zhao, G., Wu, Y., He, M., Bai, J., Cheng, J., & Lin, Y. (2019). Preprocessing and grading of glioma data acquired from multicenter. Journal of Medical Imaging and Health Informatics, 9(6), 1236–1245.

    Article  Google Scholar 

  • Zhou, B., & Buyya, R. (2018). Augmentation techniques for mobile cloud computing: A taxonomy, survey, and future directions. ACM Computing Surveys (CSUR), 51(1), 13.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The School of Mathematics and Computer Science, XinYang Vocational and Technical College, Xinyang, 464000, Henan, China

    Yi Jiang, Xiao Liu, YongHao Li & Yali Zhang

Authors
  1. Yi Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. YongHao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yali Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yali Zhang.

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/s10772-022-10003-y

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

Jiang, Y., Liu, X., Li, Y. et al. RETRACTED ARTICLE: A data layout method suitable for workflow in a cloud computing environment with speech applications. Int J Speech Technol 24, 31–40 (2021). https://doi.org/10.1007/s10772-020-09705-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10772-020-09705-y

Keywords

Search

Navigation