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dc.contributorEsposito, Flavioen_US
dc.contributorMaimaitijiang, Maitiniyazien_US
dc.contributorSagan, Vasiten_US
dc.contributorBonaiuti, Enricoen_US
dc.creatorMuhammad, Waqaren_US
dc.date2019-11-26en_US
dc.date.accessioned2019-11-28T19:06:29Z
dc.date.available2019-11-28T19:06:29Z
dc.identifierhttps://mel.cgiar.org/reporting/download/hash/28b747d7875dc6be9dca09eff33a1e75en_US
dc.identifier.citationWaqar Muhammad, Flavio Esposito, Maitiniyazi Maimaitijiang, Vasit Sagan, Enrico Bonaiuti. (26/11/2019). Polly: A Tool for Rapid Data Integration and Analysis in Support of Agricultural Research and Education.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/10425
dc.description.abstractData analysis and modeling is a complex and demanding task. While a variety of software and tools exist to cope with this problem and tame big data operations, most of these tools are either not free, and when they are, they require large amount of configuration and steep learning curve. Moreover, they provide limited functionalities. In this paper we propose Polly, an online data analysis and modeling open-source tool that is intuitive to use and can be used with minimal or no configuration. Users can use Polly to rapidly integrate, analyze their data, prototype and test their novel methodologies. Polly can be used also as an educational tool. Users can use Polly to upload or connect to their structured data sources, load the required data into our system and perform various data processing tasks. Examples of such operations include data cleaning, data pre-processing, attribute encoding, regression and classification analysis. Aside from modeling, users can then download their results in the form of graphs in several standard visualization formats. While in this paper we focus on analyzing dataset for smart farming, our tool usage fits to a more general audience. To justify our backend design and implementation choices, we also present a performance analysis between backend virtualization technologies (containers or serverless computing), showing both expected and surprising results.en_US
dc.formatPDFen_US
dc.languageenen_US
dc.publisherElsevieren_US
dc.rightsCC-BY-4.0en_US
dc.sourceInternet of Things;(2019)en_US
dc.subjectuaven_US
dc.subjectsmart farmingen_US
dc.subjectserverless computingen_US
dc.subjectnetwork virtualizationen_US
dc.subjectSoya beanen_US
dc.titlePolly: A Tool for Rapid Data Integration and Analysis in Support of Agricultural Research and Educationen_US
dc.typeJournal Articleen_US
cg.creator.idMuhammad, Waqar: 0000-0001-8842-5449en_US
cg.creator.idEsposito, Flavio: 0000-0002-7798-4584en_US
cg.creator.idSagan, Vasit: 0000-0003-4375-2096en_US
cg.creator.idBonaiuti, Enrico: 0000-0002-4010-4141en_US
cg.creator.ID-typeORCIDen_US
cg.creator.ID-typeORCIDen_US
cg.creator.ID-typeORCIDen_US
cg.creator.ID-typeORCIDen_US
cg.subject.agrovocmachine learningen_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.centerSaint Louis Universityen_US
cg.contributor.crpCRP on Grain Legumes and Dryland Cereals - GLDCen_US
cg.contributor.funderInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.funderCGIAR System Office - CGIAR - Sysen_US
cg.contributor.funderNational Science Foundation - NSFen_US
cg.contributor.project-lead-instituteInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.coverage.regionNorthern Americaen_US
cg.coverage.countryUSen_US
cg.coverage.start-date2016-08-04en_US
cg.coverage.end-date2016-08-04en_US
cg.contactwaqar.muhammad@slu.eduen_US
cg.identifier.doihttps://dx.doi.org/10.1016/j.iot.2019.100141en_US
dc.identifier.statusOpen accessen_US


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