PADL: A Modeling and Deployment Language for Advanced Analytical Services
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2020-11-24
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Multidisciplinary Digital Publishing Institute (MDPI)
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Abstract
In 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.
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Díaz-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.