Next Article in Journal
Fabrication of a Horizontal and a Vertical Large Surface Area Nanogap Electrochemical Sensor
Next Article in Special Issue
Sci-Fin: Visual Mining Spatial and Temporal Behavior Features from Social Media
Previous Article in Journal
Novel Resistance Measurement Method: Analysis of Accuracy and Thermal Dependence with Applications in Fiber Materials
Previous Article in Special Issue
Privacy-Preserving Location-Based Service Scheme for Mobile Sensing Data
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(12), 2126; doi:10.3390/s16122126

Performance Prediction of a MongoDB-Based Traceability System in Smart Factory Supply Chains

1
Department of Systems Management Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do 16419, Korea
2
Research Institute of Sustainable Manufacturing System, Korea Institute of Industrial Technology, Cheonan, Chungcheongnam-do 31056, Korea
3
Department of Industrial and Systems Engineering, Dongguk University, 3ga, Pil-dong, Jung-gu, Seoul 04620, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Yike Guo
Received: 28 September 2016 / Revised: 6 December 2016 / Accepted: 12 December 2016 / Published: 14 December 2016
(This article belongs to the Special Issue Big Data and Cloud Computing for Sensor Networks)
View Full-Text   |   Download PDF [2028 KB, uploaded 14 December 2016]   |  

Abstract

In the future, with the advent of the smart factory era, manufacturing and logistics processes will become more complex, and the complexity and criticality of traceability will further increase. This research aims at developing a performance assessment method to verify scalability when implementing traceability systems based on key technologies for smart factories, such as Internet of Things (IoT) and BigData. To this end, based on existing research, we analyzed traceability requirements and an event schema for storing traceability data in MongoDB, a document-based Not Only SQL (NoSQL) database. Next, we analyzed the algorithm of the most representative traceability query and defined a query-level performance model, which is composed of response times for the components of the traceability query algorithm. Next, this performance model was solidified as a linear regression model because the response times increase linearly by a benchmark test. Finally, for a case analysis, we applied the performance model to a virtual automobile parts logistics. As a result of the case study, we verified the scalability of a MongoDB-based traceability system and predicted the point when data node servers should be expanded in this case. The traceability system performance assessment method proposed in this research can be used as a decision-making tool for hardware capacity planning during the initial stage of construction of traceability systems and during their operational phase. View Full-Text
Keywords: traceability; NoSQL; IoT; smart factory; performance traceability; NoSQL; IoT; smart factory; performance
Figures

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Kang, Y.-S.; Park, I.-H.; Youm, S. Performance Prediction of a MongoDB-Based Traceability System in Smart Factory Supply Chains. Sensors 2016, 16, 2126.

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

1

Comments

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