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Analysis of institutional authors

Andion Jimenez, JavierAuthorDueñas López, Juan CarlosAuthor

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June 9, 2019
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Optimizing Failure Prediction Time Windows Through Genetic Algorithms and Random Forests

Publicated to: IEEE Access. 6 58307-58323 - 2018-01-01 6(), DOI: 10.1109/ACCESS.2018.2874440

Authors:

González, JMN; Jiménez, JA; López, JCD
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Affiliations

Univ Politecn Madrid, Dept Ingn Sistemas Telemat, E-28040 Madrid, Spain - Author

Abstract

Failure prediction is a key component of modern autonomic systems. A crucial decision to take when performing it is which observation window to use, this is, to decide the time period in the past that will be taken into account in order to accurately predict. Currently, this decision is a highly manual process, dependent on expert knowledge. To alleviate this problem, we propose the usage of a customized genetic algorithm alongside a machine learning technique, random forests, which optimizes a novel, multiple observation window schemes that allows for more modeling complexity than other schemes present on the literature. We validate it using ten different events extracted from two real, industrial data sets: one from a high performance computing environment and one from a computer network. We show that our algorithm creates models that optimize performance while reducing the observed time automatically with minimal user input required.
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Keywords

Artificial neural-networksEvolutionary computationFailure predictionForestryGenetic algorithmGenetic algorithmsIntegrationMachine learningMicrosoft windowsObservation windowParametersPredictive modelsRandom forestsStatisticsSupport vector machinesSvmSystems

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal IEEE Access due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2018, it was in position 23/155, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Information Systems.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-27:

  • Google Scholar: 4
  • WoS: 3
  • Scopus: 3
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-27:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 20.

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 3.

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/87187/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 146
  • Downloads: 90
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Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (NAVARRO GONZALEZ, JOSE MANUEL) and Last Author (DUEÑAS LOPEZ, JUAN CARLOS).

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Awards linked to the item

The work of J. M. Navarro was supported by the Ministerio de Educacion, Cultura y Deporte of Spain, under Grant FPU 14/03209.
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