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PADL: A Modeling and Deployment Language for Advanced Analytical Services.

Josu Díaz-de-Arcaya1, Raúl Miñón1, Ana I Torre-Bastida1

  • 1TECNALIA, Basque Research & Technology Alliance (BRTA), 48160 Derio, Spain.

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Summary
This summary is machine-generated.

This paper introduces the PADL description language to simplify machine learning in smart cities. PADL abstracts infrastructure complexities, enabling efficient data analysis for improved urban services and resource management.

Keywords:
analytical pipelinesartificial intelligence description languageedge computingmachine learning life cycle

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Area of Science:

  • Computer Science
  • Data Science
  • Urban Informatics

Background:

  • Big Data analytics and Internet of Things (IoT) devices are crucial for smart city development, enhancing citizen quality of life and optimizing resources.
  • Implementing these technologies faces challenges due to massive data volumes, device heterogeneity, geographical distribution, and complex IT infrastructures.
  • Existing solutions often struggle to manage the intricacies of distributed data processing across edge, fog, and cloud environments.

Purpose of the Study:

  • To introduce the PADL (Pipeline Definition Language) description language, designed to streamline the machine learning lifecycle in smart city contexts.
  • To provide an abstraction layer that simplifies the definition and operationalization of data science pipelines, shielding users from underlying infrastructure complexities.
  • To facilitate the development of smart city applications by simplifying the management of data flows and machine learning processes.

Main Methods:

  • Development of the PADL description language, featuring annotations for infrastructure abstraction and functionalities for monitoring, notifications, and actuation.
  • Design of tools to support the adoption of PADL in production environments.
  • Demonstration of PADL's capabilities through the creation of compliant analytical pipelines for smart city use cases like flood control and waste management.

Main Results:

  • PADL effectively simplifies the definition and operationalization of machine learning pipelines in complex, heterogeneous smart city environments.
  • The language provides a crucial abstraction layer, easing the work of data scientists and engineers by decoupling pipeline logic from specific infrastructure.
  • PADL-compliant pipelines were successfully defined and demonstrated for flood control and waste management, showcasing ease of adoption and benefits.

Conclusions:

  • PADL is a beneficial tool for defining information and process flows in smart city environments, addressing key implementation challenges.
  • The language's ability to manage distributed pipelines across edge, fog, and cloud layers makes it particularly suitable for smart city applications.
  • Adoption of PADL leads to simpler and more beneficial development of data-driven urban services.