Device Data Ingestion for Industrial Big Data Platforms with a Case Study

被引：32
|
作者：
Ji, Cun [1 ]
Shao, Qingshi [1 ]
Sun, Jiao [1 ]
Liu, Shijun [1 ,2 ]
Pan, Li [1 ,2 ]
Wu, Lei [1 ,3 ]
Yang, Chenglei [1 ,2 ]
机构：
[1] Shandong Univ, Sch Comp Sci & Technol, Jinan 250101, Peoples R China
[2] Shandong Univ, Engn Res Ctr Digital Media Technol, Jinan 250101, Peoples R China
[3] North China Univ Technol, Beijing Key Lab Integrat & Anal Large Scale Strea, Beijing 100144, Peoples R China
来源：
SENSORS | 2016年 / 16卷 / 03期
基金：
国家高技术研究发展计划(863计划); 中国国家自然科学基金;
关键词：
device data ingestion; big data; internet of things; industrial internet of things; INTERNET;
D O I：
10.3390/s16030279
中图分类号：
O65 [分析化学];
学科分类号：
070302 ; 081704 ;
摘要：
Despite having played a significant role in the Industry 4.0 era, the Internet of Things is currently faced with the challenge of how to ingest large-scale heterogeneous and multi-type device data. In response to this problem we present a heterogeneous device data ingestion model for an industrial big data platform. The model includes device templates and four strategies for data synchronization, data slicing, data splitting and data indexing, respectively. We can ingest device data from multiple sources with this heterogeneous device data ingestion model, which has been verified on our industrial big data platform. In addition, we present a case study on device data-based scenario analysis of industrial big data.
引用
收藏
页数：15
相关论文
共 50 条
  • [41] A Survey on Data-driven Performance Tuning for Big Data Analytics Platforms
    Costa, Rogerio Luis de C.
    Moreira, Jose
    Pintor, Paulo
    dos Santos, Veronica
    Lifschitz, Sergio
    BIG DATA RESEARCH, 2021, 25
  • [42] Environmental Open Data in Urban Platforms: An Approach to the Big Data Life Cycle
    Gessa, Ana
    Sancha, Pilar
    JOURNAL OF URBAN TECHNOLOGY, 2020, 27 (01) : 27 - 45
  • [43] Cloud Computing Platforms for Big Data Adoption and Analytics
    Hussain, Mohammad Jabed
    Alsadie, Deafallah
    INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2022, 22 (02): : 290 - 296
  • [44] Bringing the HPC reconstruction algorithms to Big Data platforms
    Malitsky, Nikolay
    2016 NEW YORK SCIENTIFIC DATA SUMMIT (NYSDS), 2016,
  • [45] Multisided Platforms, Big Data, and a Little Antitrust Policy
    Michael L. Katz
    Review of Industrial Organization, 2019, 54 : 695 - 716
  • [46] Multisided Platforms, Big Data, and a Little Antitrust Policy
    Katz, Michael L.
    REVIEW OF INDUSTRIAL ORGANIZATION, 2019, 54 (04) : 695 - 716
  • [47] Innovative and applied research on big data platforms of smart
    Qiu, J.
    Li, J.
    Sun, H.
    25TH INTERNATIONAL CIPA SYMPOSIUM 2015, 2015, : 257 - 261
  • [48] Performance Evaluation of Enterprise Big Data Platforms with HiBench
    Ivanov, Todor
    Niemann, Raik
    Izberovic, Sead
    Rosselli, Marten
    Tolle, Karsten
    Zicari, Roberto V.
    2015 IEEE TRUSTCOM/BIGDATASE/ISPA, VOL 2, 2015, : 120 - 127
  • [49] Big data platforms: in the lens of selection and evaluation approach
    Rouhani, Saeed
    Rotbei, Sayna
    JOURNAL OF DECISION SYSTEMS, 2022, 32 (01) : 19 - 48
  • [50] Runtime Composition for Extensible Big Data Processing Platforms
    Kimura, Kosaku
    Nomura, Yoshihide
    Tanaka, Yuka
    Kurihara, Hidetoshi
    Yamamoto, Rieko
    2015 IEEE 8TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, 2015, : 1053 - 1057
12345