Performance Prediction of a MongoDB-Based Traceability System in Smart Factory Supply Chains
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-12-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/12/2126 |
| _version_ | 1828120180062945280 |
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| author | Yong-Shin Kang Il-Ha Park Sekyoung Youm |
| author_facet | Yong-Shin Kang Il-Ha Park Sekyoung Youm |
| author_sort | Yong-Shin Kang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T14:01:05Z |
| format | Article |
| id | doaj.art-0a2ffbe554094f48bc906ddb3b1bb874 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T14:01:05Z |
| publishDate | 2016-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0a2ffbe554094f48bc906ddb3b1bb8742022-12-22T04:20:06ZengMDPI AGSensors1424-82202016-12-011612212610.3390/s16122126s16122126Performance Prediction of a MongoDB-Based Traceability System in Smart Factory Supply ChainsYong-Shin Kang0Il-Ha Park1Sekyoung Youm2Department of Systems Management Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do 16419, KoreaResearch Institute of Sustainable Manufacturing System, Korea Institute of Industrial Technology, Cheonan, Chungcheongnam-do 31056, KoreaDepartment of Industrial and Systems Engineering, Dongguk University, 3ga, Pil-dong, Jung-gu, Seoul 04620, KoreaIn 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.http://www.mdpi.com/1424-8220/16/12/2126traceabilityNoSQLIoTsmart factoryperformance |
| spellingShingle | Yong-Shin Kang Il-Ha Park Sekyoung Youm Performance Prediction of a MongoDB-Based Traceability System in Smart Factory Supply Chains Sensors traceability NoSQL IoT smart factory performance |
| title | Performance Prediction of a MongoDB-Based Traceability System in Smart Factory Supply Chains |
| title_full | Performance Prediction of a MongoDB-Based Traceability System in Smart Factory Supply Chains |
| title_fullStr | Performance Prediction of a MongoDB-Based Traceability System in Smart Factory Supply Chains |
| title_full_unstemmed | Performance Prediction of a MongoDB-Based Traceability System in Smart Factory Supply Chains |
| title_short | Performance Prediction of a MongoDB-Based Traceability System in Smart Factory Supply Chains |
| title_sort | performance prediction of a mongodb based traceability system in smart factory supply chains |
| topic | traceability NoSQL IoT smart factory performance |
| url | http://www.mdpi.com/1424-8220/16/12/2126 |
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