Next Article in Journal
The Additional Error of Inertial Sensors Induced by Hypersonic Flight Conditions
Next Article in Special Issue
Worst-Case Cooperative Jamming for Secure Communications in CIoT Networks
Previous Article in Journal
Metal Sulfides as Sensing Materials for Chemoresistive Gas Sensors
Previous Article in Special Issue
An Efficient Virtual Machine Consolidation Scheme for Multimedia Cloud Computing
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(3), 279;

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

1,2,* , 1,2
School of Computer Science & Technology, Shandong University, Jinan 250101, China
Engineering Research Center of Digital Media Technology, Shandong University, Jinan 250101, China
Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data, North China University of Technology, Beijing 100144, China
This paper is an extended version of our paper Cun, J.; Shijun, L.; Chenglei, Y.; Lei, W.; Li, P. IBDP: An Industrial Big Data Ingestion and Analysis Platform and Case Studies. In Proceedings of the 2015 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI), Beijing, China, 22–23 October 2015; pp. 223–228.
Authors to whom correspondence should be addressed.
Academic Editors: Yunchuan Sun, Antonio Jara and Shengling Wang
Received: 16 January 2016 / Revised: 15 February 2016 / Accepted: 18 February 2016 / Published: 26 February 2016
(This article belongs to the Special Issue Identification, Information & Knowledge in the Internet of Things)
Full-Text   |   PDF [1823 KB, uploaded 26 February 2016]   |  


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. View Full-Text
Keywords: big data; internet of things; industrial internet of things; device data ingestion big data; internet of things; industrial internet of things; device data ingestion

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Ji, C.; Shao, Q.; Sun, J.; Liu, S.; Pan, L.; Wu, L.; Yang, C. Device Data Ingestion for Industrial Big Data Platforms with a Case Study. Sensors 2016, 16, 279.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top