Academic Journal
Algorithmic paradigms in Big Data.
| Τίτλος: | Algorithmic paradigms in Big Data. |
|---|---|
| Συγγραφείς: | Dima, Corina |
| Πηγή: | Annals of the University Dunarea de Jos of Galati: Fascicle II, Mathematics, Physics, Theoretical Mechanics; 2025, Vol. 48 Issue 2, p103-113, 11p |
| Θεματικοί όροι: | Big data, Dimensional reduction algorithms, Online algorithms, Approximation algorithms, Federated learning, Parallel processing, Electronic data processing |
| Περίληψη: | The gigantic increase in the volume of data that needs to be stored, transmitted and processed in recent years has led to the emergence of a new category of algorithms designed specifically for what is called Big Data. Today's information is too large, too diverse and requires real-time transmission to be manipulated by classical techniques. This has led to a complete rethinking of algorithmic paradigms. In this paper we present the MapReduce paradigms, streaming algorithms, approximate structures, algorithms for large graphs, dimensionality reduction, distributed machine learning. For a better understanding we have added comparisons, intuitive explanations, mathematical formulas and pseudocode. [ABSTRACT FROM AUTHOR] |
| Copyright of Annals of the University Dunarea de Jos of Galati: Fascicle II, Mathematics, Physics, Theoretical Mechanics is the property of Dunarea de Jos University of Galati 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 |
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| Header | DbId: edb DbLabel: Complementary Index An: 191553577 RelevancyScore: 1007 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1007.33386230469 |
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| Items | – Name: Title Label: Title Group: Ti Data: Algorithmic paradigms in Big Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Dima%2C+Corina%22">Dima, Corina</searchLink> – Name: TitleSource Label: Source Group: Src Data: Annals of the University Dunarea de Jos of Galati: Fascicle II, Mathematics, Physics, Theoretical Mechanics; 2025, Vol. 48 Issue 2, p103-113, 11p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br /><searchLink fieldCode="DE" term="%22Dimensional+reduction+algorithms%22">Dimensional reduction algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Online+algorithms%22">Online algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Approximation+algorithms%22">Approximation algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Federated+learning%22">Federated learning</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+processing%22">Parallel processing</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The gigantic increase in the volume of data that needs to be stored, transmitted and processed in recent years has led to the emergence of a new category of algorithms designed specifically for what is called Big Data. Today's information is too large, too diverse and requires real-time transmission to be manipulated by classical techniques. This has led to a complete rethinking of algorithmic paradigms. In this paper we present the MapReduce paradigms, streaming algorithms, approximate structures, algorithms for large graphs, dimensionality reduction, distributed machine learning. For a better understanding we have added comparisons, intuitive explanations, mathematical formulas and pseudocode. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Annals of the University Dunarea de Jos of Galati: Fascicle II, Mathematics, Physics, Theoretical Mechanics is the property of Dunarea de Jos University of Galati 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: 10.35219/ann-ugal-math-phys-mec.2025.2.08 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 103 Subjects: – SubjectFull: Big data Type: general – SubjectFull: Dimensional reduction algorithms Type: general – SubjectFull: Online algorithms Type: general – SubjectFull: Approximation algorithms Type: general – SubjectFull: Federated learning Type: general – SubjectFull: Parallel processing Type: general – SubjectFull: Electronic data processing Type: general Titles: – TitleFull: Algorithmic paradigms in Big Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Dima, Corina IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20672071 Numbering: – Type: volume Value: 48 – Type: issue Value: 2 Titles: – TitleFull: Annals of the University Dunarea de Jos of Galati: Fascicle II, Mathematics, Physics, Theoretical Mechanics Type: main |
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