Examinando por Autor "Torre Bastida, Ana Isabel"

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    Orfeon: an AIOps framework for the goal-driven operationalization of distributed analytical pipelines
    (Elsevier B.V., 2023-03) Díaz de Arcaya Serrano, Josu; Torre Bastida, Ana Isabel; Miñón Jiménez, Raúl; Almeida, Aitor
    The use of Artificial Intelligence solutions keeps raising in the business domain. However, this adoption has not brought the expected results to companies so far. There are several reasons that make Artificial Intelligence solutions particularly complicated to adopt by businesses, such as the knowledge gap between the data science and operations teams. In this paper, we tackle the operationalization of distributed analytical pipelines in heterogeneous production environments, which span across different computational layers. In particular, we present a system called Orfeon, which can leverage different objectives and yields an optimized deployment for these pipelines. In addition, we offer the mathematical formulation of the problem alongside the objectives in hand (i.e. resilience, performance, and cost). Next, we propose a scenario utilizing cloud and edge infrastructural devices, in which we demonstrate how the system can optimize these objectives, without incurring scalability issues in terms of time nor memory. Finally, we compare the usefulness of Orfeon with a variety of tools in the field of machine learning operationalization and conclude that it is able to outperform these tools under the analyzed criteria, making it an appropriate system for the operationalization of machine learning pipelines
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    Ítem
    PADL: a modeling and deployment language for advanced analytical services
    (MDPI AG, 2020-11-24) Díaz de Arcaya Serrano, Josu; Miñón Jiménez, Raúl ; Torre Bastida, Ana Isabel; Ser Lorente, Javier del; Almeida, Aitor
    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.