Please use this identifier to cite or link to this item:
https://doi.org/10.3390/bdcc4030017
DC Field | Value | |
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dc.title | MOBDA: Microservice-oriented big data architecture for smart city transport systems | |
dc.contributor.author | Asaithambi, S.P.R. | |
dc.contributor.author | Venkatraman, R. | |
dc.contributor.author | Venkatraman, S. | |
dc.date.accessioned | 2021-08-23T03:24:15Z | |
dc.date.available | 2021-08-23T03:24:15Z | |
dc.date.issued | 2020-07-09 | |
dc.identifier.citation | Asaithambi, S.P.R., Venkatraman, R., Venkatraman, S. (2020-07-09). MOBDA: Microservice-oriented big data architecture for smart city transport systems. Big Data and Cognitive Computing 4 (3) : 1-27. ScholarBank@NUS Repository. https://doi.org/10.3390/bdcc4030017 | |
dc.identifier.issn | 25042289 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/198757 | |
dc.description.abstract | Highly populated cities depend highly on intelligent transportation systems (ITSs) for reliable and efficient resource utilization and traffic management. Current transportation systems struggle to meet different stakeholder expectations while trying their best to optimize resources in providing various transport services. This paper proposes a Microservice-Oriented Big Data Architecture (MOBDA) incorporating data processing techniques, such as predictive modelling for achieving smart transportation and analytics microservices required towards smart cities of the future. We postulate key transportation metrics applied on various sources of transportation data to serve this objective. A novel hybrid architecture is proposed to combine stream processing and batch processing of big data for a smart computation of microservice-oriented transportation metrics that can serve the different needs of stakeholders. Development of such an architecture for smart transportation and analytics will improve the predictability of transport supply for transport providers and transport authority as well as enhance consumer satisfaction during peak periods. © 2020 by the authors. | |
dc.publisher | MDPI AG | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.source | Scopus OA2020 | |
dc.subject | Big data | |
dc.subject | Data analytics | |
dc.subject | Intelligent transportation systems | |
dc.subject | Microservice-oriented big data architecture | |
dc.subject | Smart technologies | |
dc.type | Article | |
dc.contributor.department | INSTITUTE OF SYSTEMS SCIENCE | |
dc.description.doi | 10.3390/bdcc4030017 | |
dc.description.sourcetitle | Big Data and Cognitive Computing | |
dc.description.volume | 4 | |
dc.description.issue | 3 | |
dc.description.page | 1-27 | |
Appears in Collections: | Staff Publications Elements |
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