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：
中国国家自然科学基金; 国家高技术研究发展计划(863计划);
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] Big Data Processing in the Cloud - Challenges and Platforms
    Zhelev, Svetoslav
    Rozeva, Anna
    PROCEEDINGS OF THE 43RD INTERNATIONAL CONFERENCE APPLICATIONS OF MATHEMATICS IN ENGINEERING AND ECONOMICS (AMEE'17), 2017, 1910
  • [42] Popular platforms for big data analytics: A survey
    Merrouchi, Mohamed
    Skittou, Mustapha
    Gadi, Taoufiq
    2018 INTERNATIONAL CONFERENCE ON ELECTRONICS, CONTROL, OPTIMIZATION AND COMPUTER SCIENCE (ICECOCS), 2018,
  • [43] Recent Trends of Big Data Platforms and Applications
    Whang, Kyu-Young
    CONCEPTUAL MODELING, ER 2018, 2018, 11157 : 10 - 11
  • [44] AutoCompBD: Autonomic Computing and Big Data platforms
    Pop, Florin
    Dobre, Ciprian
    Costan, Alexandru
    SOFT COMPUTING, 2017, 21 (16) : 4497 - 4499
  • [45] Big Data Techniques, Systems, Applications, and Platforms: Case Studies from Academia
    Radenski, Atanas
    Gurov, Todor
    Kaloyanova, Kalinka
    Kirov, Nikolay
    Nisheva, Maria
    Stanchev, Peter
    Stoimenova, Eugenia
    PROCEEDINGS OF THE 2016 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (FEDCSIS), 2016, 8 : 883 - 888
  • [46] Data Feature Selection Methods on Distributed Big Data Processing Platforms
    Catalkaya, Mehmet Burak
    Kalipsiz, Oya
    Aktas, Mehmet S.
    Turgut, Umut Orcun
    2018 3RD INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ENGINEERING (UBMK), 2018, : 133 - 138
  • [47] BigDataNetSim: A Simulator for Data and Process Placement in Large Big Data Platforms
    de Almeida, Leandro Batista
    de Almeida, Eduardo Cunha
    Murphy, John
    De Grande, Robson E.
    Ventresque, Anthony
    PROCEEDINGS OF THE 2018 IEEE/ACM 22ND INTERNATIONAL SYMPOSIUM ON DISTRIBUTED SIMULATION AND REAL TIME APPLICATIONS (DS-RT), 2018, : 145 - 154
  • [48] Modelling big data platforms as knowledge graphs: the data platform shaper
    Greco, David
    Osborne, Francesco
    Pusceddu, Simone
    Recupero, Diego Reforgiato
    JOURNAL OF BIG DATA, 2025, 12 (01)
  • [49] Stream Processing of Scientific Big Data on Heterogeneous Platforms - Image Analytics on Big Data in Motion
    Najmabadi, S. M.
    Klaiber, M.
    Wang, Z.
    Baroud, Y.
    Simon, S.
    2013 IEEE 16TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING (CSE 2013), 2013, : 965 - 970
  • [50] Integrating Data Quality in Industrial Big Data Architectures: An Action Design Research Study
    Ustunboyacioglu, Ipek
    Kumara, Indika
    Di Nucci, Dario
    Tamburri, Damian Andrew
    van den Heuvel, Willem-Jan
    SOFTWARE ARCHITECTURE, ECSA 2024, 2024, 14889 : 3 - 19
12345