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dc.contributor.authorMitra, S.
dc.contributor.authorRoselius, M.
dc.contributor.authorAndrade-Sanchez, P.
dc.contributor.authorMcKay, J.K.
dc.contributor.authorPallickara, S.L.
dc.date.accessioned2024-08-18T22:58:14Z
dc.date.available2024-08-18T22:58:14Z
dc.date.issued2023-02-16
dc.identifier.citationMitra S, Roselius M, Andrade-Sanchez P, McKay JK, Pallickara SL. Radix+: High-throughput georeferencing and data ingestion over voluminous and fast-evolving phenotyping sensor data. Concurrency Computat Pract Exper. 2023; 35(8):e7484. doi: 10.1002/cpe.7484
dc.identifier.issn1532-0626
dc.identifier.doi10.1002/cpe.7484
dc.identifier.urihttp://hdl.handle.net/10150/674655
dc.description.abstractRemote sensing of plant traits and their environment facilitates non-invasive, high-throughput monitoring of the plant's physiological characteristics. However, voluminous observational data generated by such autonomous sensor networks overwhelms scientific users when they have to analyze the data. In order to provide a scalable and effective analysis environment, there is a need for storage and analytics that support high-throughput data ingestion while preserving spatiotemporal and sensor-specific characteristics. Also, the framework should enable modelers and scientists to run their analytics while coping with the fast and continuously evolving nature of the dataset. In this paper, we present Radix+, a high-throughput distributed data storage system for supporting scalable georeferencing, and interactive query-based spatiotemporal analytics with trackable data integrity. We include empirical evaluations performed on a commodity machine cluster with up to 1 TB of data. Our benchmarks demonstrate subsecond latency for majority of our evaluated queries and (Formula presented.) improvement in data ingestion rate over systems such as Geomesa. © 2023 The Authors. Concurrency and Computation: Practice and Experience published by John Wiley & Sons Ltd.
dc.language.isoen
dc.publisherJohn Wiley and Sons Ltd
dc.rights© 2023 The Authors. Concurrency and Computation: Practice and Experience published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectdata ingestion
dc.subjectdistributed analytics
dc.subjectgeoreferencing
dc.subjecthigh-throughput phenotyping
dc.subjectsensor
dc.subjectvisualization
dc.titleRadix+: High-throughput georeferencing and data ingestion over voluminous and fast-evolving phenotyping sensor data
dc.typeArticle
dc.typetext
dc.contributor.departmentDepartment of Biosystems Engineering, University of Arizona
dc.identifier.journalConcurrency and Computation: Practice and Experience
dc.description.noteOpen access article
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
dc.eprint.versionFinal Published Version
dc.source.journaltitleConcurrency and Computation: Practice and Experience
refterms.dateFOA2024-08-18T22:58:14Z


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© 2023 The Authors. Concurrency and Computation: Practice and Experience published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License.
Except where otherwise noted, this item's license is described as © 2023 The Authors. Concurrency and Computation: Practice and Experience published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License.