@article{nikolakis_microservice_2020,
	series = {30th {International} {Conference} on {Flexible} {Automation} and {Intelligent} {Manufacturing} ({FAIM2021})},
	title = {A microservice architecture for predictive analytics in manufacturing},
	volume = {51},
	issn = {2351-9789},
	url = {http://www.sciencedirect.com/science/article/pii/S2351978920320102},
	doi = {10.1016/j.promfg.2020.10.153},
	abstract = {This paper discusses on the design, development and deployment of a flexible and modular platform supporting smart predictive maintenance operations, enabled by microservices architecture and virtualization technologies. Virtualization allows the platform to be deployed in a multi-tenant environment, while facilitating resource isolation and independency from specific technologies or services. Moreover, the proposed platform supports scalable data storage supporting an effective and efficient management of large volume of Industry 4.0 data. Methodologies of data-driven predictive maintenance are provided to the user as-a-service, facilitating offline training and online execution of pre-trained analytics models, while the connection of the raw data to contextual information support their understanding and interpretation, while guaranteeing interoperability across heterogeneous systems. A use case related to the predictive maintenance operations of a robotic manipulator is examined to demonstrate the effectiveness and the efficiency of the proposed platform.},
	language = {en},
	urldate = {2020-11-23},
	journal = {Procedia Manufacturing},
	author = {Nikolakis, N. and Marguglio, A. and Veneziano, G. and Greco, P. and Panicucci, S. and Cerquitelli, T. and Macii, E. and Andolina, S. and Alexopoulos, K.},
	month = jan,
	year = {2020},
	keywords = {Machine learning, Microservice architecture, Play approach, Plug, Robotics industry, Service-oriented platform},
	pages = {1091--1097},
}

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A use case related to the predictive maintenance operations of a robotic manipulator is examined to demonstrate the effectiveness and the efficiency of the proposed platform.","language":"en","urldate":"2020-11-23","journal":"Procedia Manufacturing","author":[{"propositions":[],"lastnames":["Nikolakis"],"firstnames":["N."],"suffixes":[]},{"propositions":[],"lastnames":["Marguglio"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["Veneziano"],"firstnames":["G."],"suffixes":[]},{"propositions":[],"lastnames":["Greco"],"firstnames":["P."],"suffixes":[]},{"propositions":[],"lastnames":["Panicucci"],"firstnames":["S."],"suffixes":[]},{"propositions":[],"lastnames":["Cerquitelli"],"firstnames":["T."],"suffixes":[]},{"propositions":[],"lastnames":["Macii"],"firstnames":["E."],"suffixes":[]},{"propositions":[],"lastnames":["Andolina"],"firstnames":["S."],"suffixes":[]},{"propositions":[],"lastnames":["Alexopoulos"],"firstnames":["K."],"suffixes":[]}],"month":"January","year":"2020","keywords":"Machine learning, Microservice architecture, Play approach, Plug, Robotics industry, Service-oriented platform","pages":"1091–1097","bibtex":"@article{nikolakis_microservice_2020,\n\tseries = {30th {International} {Conference} on {Flexible} {Automation} and {Intelligent} {Manufacturing} ({FAIM2021})},\n\ttitle = {A microservice architecture for predictive analytics in manufacturing},\n\tvolume = {51},\n\tissn = {2351-9789},\n\turl = {http://www.sciencedirect.com/science/article/pii/S2351978920320102},\n\tdoi = {10.1016/j.promfg.2020.10.153},\n\tabstract = {This paper discusses on the design, development and deployment of a flexible and modular platform supporting smart predictive maintenance operations, enabled by microservices architecture and virtualization technologies. Virtualization allows the platform to be deployed in a multi-tenant environment, while facilitating resource isolation and independency from specific technologies or services. Moreover, the proposed platform supports scalable data storage supporting an effective and efficient management of large volume of Industry 4.0 data. Methodologies of data-driven predictive maintenance are provided to the user as-a-service, facilitating offline training and online execution of pre-trained analytics models, while the connection of the raw data to contextual information support their understanding and interpretation, while guaranteeing interoperability across heterogeneous systems. A use case related to the predictive maintenance operations of a robotic manipulator is examined to demonstrate the effectiveness and the efficiency of the proposed platform.},\n\tlanguage = {en},\n\turldate = {2020-11-23},\n\tjournal = {Procedia Manufacturing},\n\tauthor = {Nikolakis, N. and Marguglio, A. and Veneziano, G. and Greco, P. and Panicucci, S. and Cerquitelli, T. and Macii, E. and Andolina, S. and Alexopoulos, K.},\n\tmonth = jan,\n\tyear = {2020},\n\tkeywords = {Machine learning, Microservice architecture, Play approach, Plug, Robotics industry, Service-oriented platform},\n\tpages = {1091--1097},\n}\n\n","author_short":["Nikolakis, N.","Marguglio, A.","Veneziano, G.","Greco, P.","Panicucci, S.","Cerquitelli, T.","Macii, E.","Andolina, S.","Alexopoulos, K."],"key":"nikolakis_microservice_2020","id":"nikolakis_microservice_2020","bibbaseid":"nikolakis-marguglio-veneziano-greco-panicucci-cerquitelli-macii-andolina-etal-amicroservicearchitectureforpredictiveanalyticsinmanufacturing-2020","role":"author","urls":{"Paper":"http://www.sciencedirect.com/science/article/pii/S2351978920320102"},"keyword":["Machine learning","Microservice architecture","Play approach","Plug","Robotics industry","Service-oriented platform"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["yanwtMpCcFaHzRwWb","iwKepCrWBps7ojhDx"],"keywords":["machine learning","microservice architecture","play approach","plug","robotics industry","service-oriented platform"],"search_terms":["microservice","architecture","predictive","analytics","manufacturing","nikolakis","marguglio","veneziano","greco","panicucci","cerquitelli","macii","andolina","alexopoulos"],"title":"A microservice architecture for predictive analytics in manufacturing","year":2020}