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] |
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| Βάση Δεδομένων: | Complementary Index |
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