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Unstructured big data analysis algorithm and simulation of Internet of Things based on machine learning

  • S.I. : ATCI 2019
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Big data values data processing to ensure effective value-added data. With the rapid development of the cloud era, the coverage of big data has gradually expanded, and it has received wide attention from all walks of life. In the process of modern social development, big data analysis is gradually applied to the future development planning, risk evaluation and integration of market development status. With the rapid development of many fields of society, the flow of information has gradually expanded, and the Internet has developed more rapidly, prompting the application of big data in various fields. Machine learning is a multidisciplinary study of how computers use data or past experience. With the ability to independently improve specific algorithms, the computer acquires knowledge through learning and achieves the goal of artificial intelligence. Big data and machine learning are the major technological changes in the modern computer world, and these technologies have had a huge impact on all walks of life. At present, with the rapid development of the Internet, mobile communications, social networks and the Internet of Things, these networks generate large amounts of data every day, and data become the most important information resource of today. Some studies have shown that in many cases, the larger the amount of data, the better the data will be for machine learning. On this basis, this paper proposes an online client algorithm based on machine learning algorithm for IoT unstructured big data analysis and uses it in other big data analysis scenarios. Use the online data entered by the customer to implement background data mining, the parallel way to verify its efficiency through machine learning algorithms such as K-nearest neighbor algorithm.

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References

  1. Yang C, Huang Q, Li Z et al (2017) Big Data and cloud computing: innovation opportunities and challenges. Int J Digital Earth 10(1):13–53

    Article  Google Scholar 

  2. Akter S, Wamba SF (2017) Big data and disaster management: a systematic review and agenda for future research. Ann Oper Res 9:1–21

    Google Scholar 

  3. Chaurasia SS, Rosin AF (2017) From Big Data to Big Impact: analytics for teaching and learning in higher education. Ind Commer Train 49(7/8):321–328

    Article  Google Scholar 

  4. Zhang Y, Qiu M, Tsai CW et al (2017) Health-CPS: healthcare cyber-physical system assisted by cloud and big data. IEEE Syst J 11(1):88–95

    Article  Google Scholar 

  5. Zhao L, Chen Z, Hu Y et al (2018) Distributed feature selection for efficient economic big data analysis. IEEE Trans Big Data PP(99):1–1

  6. Kim JH (2017) A review of cyber-physical system research relevant to the emerging IT trends: industry 4.0, IoT, Big Data, and Cloud Computing. J Ind Integr Manag 2(3):1750011

    Article  Google Scholar 

  7. Sharma PK, Chen M-Y, Park JH (2017) A software defined fog node based distributed blockchain cloud architecture for IoT. IEEE Access PP(99):1–1

  8. Pérez H, Gutiérrez JJ, Peiró S et al (2016) Distributed architecture for developing mixed-criticality systems in multi-core platforms. J Syst Softw 123:145–159

    Article  Google Scholar 

  9. Yuan J, Holtz C, Smith T et al (2017) Autism spectrum disorder detection from semi-structured and unstructured medical data. EURASIP J Bioinf Syst Biol 1:3–12

    Google Scholar 

  10. Banowosari LY, Purnamasari D (2016) Approach for unwrapping the unstructured to structured data the case of classified ads in HTML format. Adv Sci Lett 22(8):1909–1913

    Article  Google Scholar 

  11. Lai L, Lu Q, Lin X et al (2017) Scalable subgraph enumeration in MapReduce: a cost-oriented approach. VLDB J Int J Very Large Data Bases 26(3):421–446

    Article  Google Scholar 

  12. Cheng D, Jia R, Guo Y et al (2017) Improving performance of heterogeneous MapReduce clusters with adaptive task tuning. IEEE Trans Parallel Distrib Syst 28(3):774–786

    Article  Google Scholar 

  13. Zhang Y, Wen J (2017) The IoT electric business model: using blockchain technology for the internet of things. Peer-to-Peer Netw Appl 10(4):983–994

    Article  Google Scholar 

  14. Schulz P, Matthe M, Klessig H et al (2017) Latency critical IoT applications in 5G: perspective on the design of radio interface and network architecture. IEEE Commun Mag 55(2):70–78

    Article  Google Scholar 

  15. Mehdi M, Al-Fuqaha A, Sorour S et al (2018) Deep learning for IoT big data and streaming analytics: a survey. IEEE Commun Surv Tutor 20(4):2923–2960

    Article  Google Scholar 

  16. Zhu D (2018) Deep learning over IoT big data-based ubiquitous parking guidance robot for parking near destination especially hospital. Pers Ubiquit Comput 4:1–8

    Google Scholar 

  17. Liu C, Yang C, Zhang X et al (2015) External integrity verification for outsourced big data in cloud and IoT: a big picture. Future Gener Comput Syst 49(C):58–67

    Article  Google Scholar 

  18. Zhang Q, Yang LT, Chen Z et al (2018) High-order possibilistic c-means algorithms based on tensor decompositions for big data in IoT. Inf Fusion 39:72–80

    Article  Google Scholar 

  19. Vijaykumar S, Saravanakumar SG, Balamurugan M (2015) Unique Sense: smart computing prototype for industry 4.0 revolution with IOT and Bigdata implementation model. Indian J Sci Technol 8(35):1–4

    Article  Google Scholar 

  20. Bi Z (2017) Embracing internet of things (iot) and big data for industrial informatics. Enterp Inf Syst 123:145–159

    Google Scholar 

  21. Yeu CWT, Lim M-H, Huang G-B et al (2012) A new machine learning paradigm for terrain reconstruction. IEEE Geosci Remote Sens Lett 3(3):382–386

    Article  Google Scholar 

  22. Hassan MK, El-Desouky AI, Badawy MM, Sarhan AM, Elhoseny M, Manogaran G (2019) EoT-driven hybrid ambient assisted living framework with naïve Bayes-firefly algorithm. Neural Comput Appl 31(5):1275–1300

    Article  Google Scholar 

Download references

Funding

This study was supported by National Key R&D Plan (Grant No. 2018YFB0605504), Fundamental Research Funds for the Central Universities (Grant No. JB2019078), the Postdoctoral Innovative Talent Support Program of China (Grant No. BX20180098), and the China Postdoctoral Science Foundation (Grant No. 2018M640102).

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Authors and Affiliations

  1. School of Economics and Management, North China Electric Power University, Beijing, 102206, People’s Republic of China

    Rui Hou

  2. School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing, 102206, People’s Republic of China

    YanQiang Kong

  3. State Grid Ningxia Electric Power Co., Ltd, Yinchuan, 750001, Ningxia, People’s Republic of China

    Bing Cai

  4. State-Owned Assets Supervision and Administration Commission, Yinchuan, 750000, Ningxia, People’s Republic of China

    Huan Liu

Authors
  1. Rui Hou
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  2. YanQiang Kong
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  3. Bing Cai
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  4. Huan Liu
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Correspondence to YanQiang Kong.

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Hou, R., Kong, Y., Cai, B. et al. Unstructured big data analysis algorithm and simulation of Internet of Things based on machine learning. Neural Comput & Applic 32, 5399–5407 (2020). https://doi.org/10.1007/s00521-019-04682-z

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  • DOI: https://doi.org/10.1007/s00521-019-04682-z

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