FADTESE: A framework for automated deployment and effectiveness evaluation for big data tools.
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| Title: | FADTESE: A framework for automated deployment and effectiveness evaluation for big data tools. |
|---|---|
| Authors: | Ho, Mony1 mho.phdscholar@lincoln.edu.my, Ang, Sokroeurn1 angsokroeurn.phdscholar@lincoln.edu.my, Huy, Sopheatra1 hsopheaktra.phdscholar@lincoln.edu.my, Janarthanan, Midhunchakkaravarthy1 midhun@lincoln.edu.my |
| Source: | International Journal of Electrical & Computer Engineering (2088-8708). Apr2026, Vol. 16 Issue 2, p1051-1062. 12p. |
| Subjects: | Satisfaction, Linux operating systems, Software development tools, Evaluation research, Distributed computing, Python programming language |
| Abstract: | Manual deployment of big data tools such as Hadoop, Sqoop, and Python is often slow, complex, and error prone because of extensive configuration steps, dependency conflicts, and inconsistent command-line execution. These challenges lead to unreliable installations and variations across systems. This study introduces framework for automated deployment and time, error, satisfaction evaluation (FADTESE), a unified framework that automates the installation of big data tools and evaluates its performance. The framework consists of two integrated components. The first is the automated deployment model, which validates environment readiness using the automation deployment readiness index (ADRI) and achieved a readiness value of 1.0 in this study. The second is the time, error, and satisfaction evaluation model, which quantifies improvements gained from automation and produced a score of 0.5941 through bootstrap resampling with ten thousand samples, indicating moderate effectiveness. The FADTESE script was technically validated across multiple Linux environments, including Ubuntu, Linux Mint, and AWS Ubuntu server systems. The performance evaluation involving eighty IT practitioners was conducted on Ubuntu systems to ensure consistent testing conditions and confirmed substantial gains in installation time, error reduction, and user satisfaction. Combining readiness and effectiveness yields a composite score of 0.5941 or 59.41%. FADTESE provides a reproducible and data driven method that standardizes big data deployment and improves reliability across local and cloud-based Linux environments. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192718368 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: FADTESE: A framework for automated deployment and effectiveness evaluation for big data tools. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ho%2C+Mony%22">Ho, Mony</searchLink><relatesTo>1</relatesTo><i> mho.phdscholar@lincoln.edu.my</i><br /><searchLink fieldCode="AR" term="%22Ang%2C+Sokroeurn%22">Ang, Sokroeurn</searchLink><relatesTo>1</relatesTo><i> angsokroeurn.phdscholar@lincoln.edu.my</i><br /><searchLink fieldCode="AR" term="%22Huy%2C+Sopheatra%22">Huy, Sopheatra</searchLink><relatesTo>1</relatesTo><i> hsopheaktra.phdscholar@lincoln.edu.my</i><br /><searchLink fieldCode="AR" term="%22Janarthanan%2C+Midhunchakkaravarthy%22">Janarthanan, Midhunchakkaravarthy</searchLink><relatesTo>1</relatesTo><i> midhun@lincoln.edu.my</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Electrical+%26+Computer+Engineering+%282088-8708%29%22">International Journal of Electrical & Computer Engineering (2088-8708)</searchLink>. Apr2026, Vol. 16 Issue 2, p1051-1062. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Satisfaction%22">Satisfaction</searchLink><br /><searchLink fieldCode="DE" term="%22Linux+operating+systems%22">Linux operating systems</searchLink><br /><searchLink fieldCode="DE" term="%22Software+development+tools%22">Software development tools</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+research%22">Evaluation research</searchLink><br /><searchLink fieldCode="DE" term="%22Distributed+computing%22">Distributed computing</searchLink><br /><searchLink fieldCode="DE" term="%22Python+programming+language%22">Python programming language</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Manual deployment of big data tools such as Hadoop, Sqoop, and Python is often slow, complex, and error prone because of extensive configuration steps, dependency conflicts, and inconsistent command-line execution. These challenges lead to unreliable installations and variations across systems. This study introduces framework for automated deployment and time, error, satisfaction evaluation (FADTESE), a unified framework that automates the installation of big data tools and evaluates its performance. The framework consists of two integrated components. The first is the automated deployment model, which validates environment readiness using the automation deployment readiness index (ADRI) and achieved a readiness value of 1.0 in this study. The second is the time, error, and satisfaction evaluation model, which quantifies improvements gained from automation and produced a score of 0.5941 through bootstrap resampling with ten thousand samples, indicating moderate effectiveness. The FADTESE script was technically validated across multiple Linux environments, including Ubuntu, Linux Mint, and AWS Ubuntu server systems. The performance evaluation involving eighty IT practitioners was conducted on Ubuntu systems to ensure consistent testing conditions and confirmed substantial gains in installation time, error reduction, and user satisfaction. Combining readiness and effectiveness yields a composite score of 0.5941 or 59.41%. FADTESE provides a reproducible and data driven method that standardizes big data deployment and improves reliability across local and cloud-based Linux environments. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.11591/ijece.v16i2.pp1051-1062 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1051 Subjects: – SubjectFull: Satisfaction Type: general – SubjectFull: Linux operating systems Type: general – SubjectFull: Software development tools Type: general – SubjectFull: Evaluation research Type: general – SubjectFull: Distributed computing Type: general – SubjectFull: Python programming language Type: general Titles: – TitleFull: FADTESE: A framework for automated deployment and effectiveness evaluation for big data tools. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ho, Mony – PersonEntity: Name: NameFull: Ang, Sokroeurn – PersonEntity: Name: NameFull: Huy, Sopheatra – PersonEntity: Name: NameFull: Janarthanan, Midhunchakkaravarthy IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20888708 Numbering: – Type: volume Value: 16 – Type: issue Value: 2 Titles: – TitleFull: International Journal of Electrical & Computer Engineering (2088-8708) Type: main |
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