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dc.contributor.authorDíaz-de-Arcaya, Josu
dc.contributor.authorMiñón, Raúl
dc.contributor.authorTorre-Bastida, Ana I.
dc.contributor.authorDel Ser, Javier
dc.contributor.authorAlmeida, Aitor
dc.date.accessioned2020-12-04T08:53:41Z
dc.date.available2020-12-04T08:53:41Z
dc.date.issued2020-11-24
dc.identifier.citationDíaz-de-Arcaya, Josu, Raúl Miñón, Ana I. Torre-Bastida, Javier Del Ser, and Aitor Almeida. “PADL: A Modeling and Deployment Language for Advanced Analytical Services.” Sensors 20, no. 23 (November 24, 2020): 6712. doi:10.3390/s20236712.en
dc.identifier.urihttp://hdl.handle.net/11556/1027
dc.description.abstractIn the smart city context, Big Data analytics plays an important role in processing the data collected through IoT devices. The analysis of the information gathered by sensors favors the generation of specific services and systems that not only improve the quality of life of the citizens, but also optimize the city resources. However, the difficulties of implementing this entire process in real scenarios are manifold, including the huge amount and heterogeneity of the devices, their geographical distribution, and the complexity of the necessary IT infrastructures. For this reason, the main contribution of this paper is the PADL description language, which has been specifically tailored to assist in the definition and operationalization phases of the machine learning life cycle. It provides annotations that serve as an abstraction layer from the underlying infrastructure and technologies, hence facilitating the work of data scientists and engineers. Due to its proficiency in the operationalization of distributed pipelines over edge, fog, and cloud layers, it is particularly useful in the complex and heterogeneous environments of smart cities. For this purpose, PADL contains functionalities for the specification of monitoring, notifications, and actuation capabilities. In addition, we provide tools that facilitate its adoption in production environments. Finally, we showcase the usefulness of the language by showing the definition of PADL-compliant analytical pipelines over two uses cases in a smart city context (flood control and waste management), demonstrating that its adoption is simple and beneficial for the definition of information and process flows in such environments.en
dc.description.sponsorshipThis work was partially supported by the SPRI–Basque Government through their ELKARTEK program (3KIA project, ref. KK-2020/00049). Aitor Almeida’s participation was supported by the FuturAAL-Ego project (RTI2018-101045-A-C22) granted by the Spanish Ministry of Science, Innovation and Universities. Javier Del Ser also acknowledges funding support from the Consolidated Research Group MATHMODE (IT1294-19), granted by the Department of Education of the Basque Government.en
dc.language.isoengen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titlePADL: A Modeling and Deployment Language for Advanced Analytical Servicesen
dc.typearticleen
dc.identifier.doi10.3390/s20236712en
dc.rights.accessRightsopenAccessen
dc.subject.keywordsEdge computingen
dc.subject.keywordsAnalytical pipelinesen
dc.subject.keywordsMachine learning life cycleen
dc.subject.keywordsArtificial intelligence description languageen
dc.identifier.essn1424-8220en
dc.issue.number23en
dc.journal.titleSensorsen
dc.page.initial6712en
dc.volume.number20en


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