location:Home > 2019 VOL.2 Aug No.4 > Adaptive data acquisition technology for android system based on big data analysis

2019 VOL.2 Aug No.4

  • Title: Adaptive data acquisition technology for android system based on big data analysis
  • Name: Goddard Patrick
  • Company: The University of Sussex
  • Abstract:

    The traditional transportation data has the disadvantage of low acquisition accuracy when performing adaptive acquisition. For this reason, the adaptive data acquisition technology analysis of android system based on big data analysis is proposed. Introduce the android system in big data analysis, build the processing framework of android system for transmitting data, realize the effective collection of transportation data; rely on the algorithm of collecting transportation data to complete the collection of common data, valuable data and invalid data. The test data shows that the proposed android system transmission data adaptive acquisition technology, the analysis accuracy rate is up to 50%, and can effectively collect the corresponding transportation data.

  • Keyword: android system; adaptive acquisition; data transmission; big data analysis;
  • DOI: 10.12250/jpciams2019040131
  • Citation form: Goddard Patrick.Adaptive data acquisition technology for android system based on big data analysis[J]. Computer Informatization and Mechanical System, 2019, vol. 2, pp. 6-10.
Reference:

[1] Ma Xiangchun, Zhong Shaochun, Xu Da. A study on the support model and implementation mechanism of individualized adaptive learning system from a large data perspective [J]. Chinese electrification education, 2017 ,17(14): 197-202.

[2] Shi Jinmei, Xia Wei. Design and Implementation of the Optimal Course Selection Model for Students Based on Large Data Analysis [J].Modern Electronic Technology, 2017, 40 (14): 130-132.

[3] Chen Xuegang. The rapid self clustering method of micro-blog public opinion based on big data technology. [J]. intelligence magazine, 2017, 36 (25):113-117.

[4] Yan Lei, Qi Bing. Research on Large Data Mining Technology of Mobile Learning System Basedon Android [J].Modern Electronic Technology, 2017, 40 (19): 142-144.

[5] Ma Liangu, Du Feng, Huang Shoumeng.Designof MOOC Intelligent Autonomous Learning SystemBased on Large Data Analysis[J].Modern ElectronicTechnology, 2017,40(20): 164-166.

[6] Hu Jun, Yin Liqun, Li Zhen, et al. Fault diagnosis method for power transmission and transformation equipment based on large data mining [J]. High voltage technology, 2017, 43 (11): 3690-3697.

[7] Liu Yan, Zhang Yongping, Zhu Cheng, et al. [J].Journal of Communications, 2017,37 (s2): 129-138.

[8] Chao, Ji Genlin, Zhao Bin.Adaptive Flow LargeData Learning Algorithm Based on Incremental Tangent Space Calibration[J].Computer Research and Development, 2017, 54 (11): 2547-2557.

[9] Cao Zhejing, long Ying. The method and practice of data adaptive urban design: as an example ofthe design of the slow line system in the historicalblock of Shanghai.[J]. city planning, 2017 ,78(14):167-168.

[10] Song Chunyu, He Hanwu, Chen He'en, Wang Zhuang-lian. Android's Unmarked Augmented Reality Registration Algorithm [J]. Computer Simulation, 2014, 31 (08): 432-437.


 


Tsuruta Institute of Medical Information Technology
Address:[502,5-47-6], Tsuyama, Tsukuba, Saitama, Japan TEL:008148-28809 fax:008148-28808 Japan,Email:jpciams@hotmail.com,2019-09-16