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A computational pipeline for the IUCN risk assessment for Meso-American reef ecosystem

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Titel: A computational pipeline for the IUCN risk assessment for Meso-American reef ecosystem
Autoren: Hoang Nguyen, Lucie Bland, Tristan Roberts, Siddeswara Guru, Minh Dinh, David Abramson
Publikationsjahr: 2017
Schlagwörter: Other Indigenous data, methodologies and global Indigenous studies not elsewhere classified, Distributed computing and systems software not elsewhere classified, Apache Spark, Ecosystem risk assessment, Many task computing, Scientific Workflow
Beschreibung: Coral reefs are of global economic and biological significance but are subject to increasing threats. As a result, it is essential to understand the risk of coral reef ecosystem collapse and to develop assessment process for those ecosystems. The International Union for Conservation of Nature (IUCN) Red List of Ecosystem (RLE) is a framework to assess the vulnerability of an ecosystem. Importantly, the assessment processes need to be repeatable as new monitoring data arises. The repeatability will also enhance transparency. In this paper, we discuss the evolution of a computational pipeline for risk assessment of the Meso-American reef ecosystem, a diverse reef ecosystem located in the Caribbean, with the focus on improving the execution time starting from sequential and parallel implementation and finally using Apache Spark. The final form of the pipeline is a scientific workflow to improve its repeatability and reproducibility.
Publikationsart: conference object
Sprache: unknown
Relation: 10779/rmit.27399264.v1
Verfügbarkeit: https://figshare.com/articles/conference_contribution/A_computational_pipeline_for_the_IUCN_risk_assessment_for_Meso-American_reef_ecosystem/27399264
Rights: All rights reserved
Dokumentencode: edsbas.AC5990E8
Datenbank: BASE
Beschreibung
Abstract:Coral reefs are of global economic and biological significance but are subject to increasing threats. As a result, it is essential to understand the risk of coral reef ecosystem collapse and to develop assessment process for those ecosystems. The International Union for Conservation of Nature (IUCN) Red List of Ecosystem (RLE) is a framework to assess the vulnerability of an ecosystem. Importantly, the assessment processes need to be repeatable as new monitoring data arises. The repeatability will also enhance transparency. In this paper, we discuss the evolution of a computational pipeline for risk assessment of the Meso-American reef ecosystem, a diverse reef ecosystem located in the Caribbean, with the focus on improving the execution time starting from sequential and parallel implementation and finally using Apache Spark. The final form of the pipeline is a scientific workflow to improve its repeatability and reproducibility.