Academic Journal
A Framework for Rapidly Prototyping Data Mining Pipelines.
| Τίτλος: | A Framework for Rapidly Prototyping Data Mining Pipelines. |
|---|---|
| Συγγραφείς: | Corradini, Flavio, Mozzoni, Luca, Piangerelli, Marco, Re, Barbara, Rossi, Lorenzo |
| Πηγή: | Big Data & Cognitive Computing; Jun2025, Vol. 9 Issue 6, p150, 28p |
| Θεματικοί όροι: | Data mining, Rapid prototyping, Data management, SWOT analysis, Big data |
| Περίληψη: | With the advent of Big Data, data mining techniques have become crucial for improving decision-making across diverse sectors, yet their employment demands significant resources and time. Time is critical in industrial contexts, as delays can lead to increased costs, missed opportunities, and reduced competitive advantage. To address this, systems for analyzing data can help prototype data mining pipelines, mitigating the risks of failure and resource wastage, especially when experimenting with novel techniques. Moreover, business experts often lack deep technical expertise and need robust support to validate their pipeline designs quickly. This paper presents Rainfall, a novel framework for rapidly prototyping data mining pipelines, developed through collaborative projects with industry. The framework's requirements stem from a combination of literature review findings, iterative industry engagement, and analysis of existing tools. Rainfall enables the visual programming, execution, monitoring, and management of data mining pipelines, lowering the barrier for non-technical users. Pipelines are composed of configurable nodes that encapsulate functionalities from popular libraries or custom user-defined code, fostering experimentation. The framework is evaluated through a case study and SWOT analysis with INGKA, a large-scale industry partner, alongside usability testing with real users and validation against scenarios from the literature. The paper then underscores the value of industry–academia collaboration in bridging theoretical innovation with practical application. [ABSTRACT FROM AUTHOR] |
| Copyright of Big Data & Cognitive Computing is the property of MDPI 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=25042289&ISBN=&volume=9&issue=6&date=20250601&spage=150&pages=150-177&title=Big Data & Cognitive Computing&atitle=A%20Framework%20for%20Rapidly%20Prototyping%20Data%20Mining%20Pipelines.&aulast=Corradini%2C%20Flavio&id=DOI:10.3390/bdcc9060150 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: A Framework for Rapidly Prototyping Data Mining Pipelines. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Corradini%2C+Flavio%22">Corradini, Flavio</searchLink><br /><searchLink fieldCode="AR" term="%22Mozzoni%2C+Luca%22">Mozzoni, Luca</searchLink><br /><searchLink fieldCode="AR" term="%22Piangerelli%2C+Marco%22">Piangerelli, Marco</searchLink><br /><searchLink fieldCode="AR" term="%22Re%2C+Barbara%22">Re, Barbara</searchLink><br /><searchLink fieldCode="AR" term="%22Rossi%2C+Lorenzo%22">Rossi, Lorenzo</searchLink> – Name: TitleSource Label: Source Group: Src Data: Big Data & Cognitive Computing; Jun2025, Vol. 9 Issue 6, p150, 28p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Rapid+prototyping%22">Rapid prototyping</searchLink><br /><searchLink fieldCode="DE" term="%22Data+management%22">Data management</searchLink><br /><searchLink fieldCode="DE" term="%22SWOT+analysis%22">SWOT analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: With the advent of Big Data, data mining techniques have become crucial for improving decision-making across diverse sectors, yet their employment demands significant resources and time. Time is critical in industrial contexts, as delays can lead to increased costs, missed opportunities, and reduced competitive advantage. To address this, systems for analyzing data can help prototype data mining pipelines, mitigating the risks of failure and resource wastage, especially when experimenting with novel techniques. Moreover, business experts often lack deep technical expertise and need robust support to validate their pipeline designs quickly. This paper presents Rainfall, a novel framework for rapidly prototyping data mining pipelines, developed through collaborative projects with industry. The framework's requirements stem from a combination of literature review findings, iterative industry engagement, and analysis of existing tools. Rainfall enables the visual programming, execution, monitoring, and management of data mining pipelines, lowering the barrier for non-technical users. Pipelines are composed of configurable nodes that encapsulate functionalities from popular libraries or custom user-defined code, fostering experimentation. The framework is evaluated through a case study and SWOT analysis with INGKA, a large-scale industry partner, alongside usability testing with real users and validation against scenarios from the literature. The paper then underscores the value of industry–academia collaboration in bridging theoretical innovation with practical application. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Big Data & Cognitive Computing is the property of MDPI 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: 10.3390/bdcc9060150 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 28 StartPage: 150 Subjects: – SubjectFull: Data mining Type: general – SubjectFull: Rapid prototyping Type: general – SubjectFull: Data management Type: general – SubjectFull: SWOT analysis Type: general – SubjectFull: Big data Type: general Titles: – TitleFull: A Framework for Rapidly Prototyping Data Mining Pipelines. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Corradini, Flavio – PersonEntity: Name: NameFull: Mozzoni, Luca – PersonEntity: Name: NameFull: Piangerelli, Marco – PersonEntity: Name: NameFull: Re, Barbara – PersonEntity: Name: NameFull: Rossi, Lorenzo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 25042289 Numbering: – Type: volume Value: 9 – Type: issue Value: 6 Titles: – TitleFull: Big Data & Cognitive Computing Type: main |
| ResultId | 1 |