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Scalability of data science algorithms; Empowering big data analytics (Record no. 132685)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01592nam a2200133 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250901b |||||||| |||| 00| 0 eng d |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Sangarsu Raghavendra* |
| 245 ## - TITLE STATEMENT | |
| Title | Scalability of data science algorithms; Empowering big data analytics |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | PP1-13 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. biblio.abstract | Scalable data science algorithms are required in the dynamic eld of big data analytics due to the<br/>exponential growth of data, in order to eciently extract valuable insights. In order to overcome the di<br/>culties presented by large datasets, this research investigates the critical role that scalable algorithms<br/>play. e study explores machine learning methods designed forlarge data analytics, distributed<br/>computing, and parallelization strategies. It starts with the constraints of standard algorithms and<br/>ends with the revolutionary inuence of scalability on practical applications. e actual use of scalable<br/>techniques is demonstrated through case studies from prominent industry players, including Google,<br/>Facebook, and Amazon.ese case studies highlight improved decision-making and superior business<br/>strategies. cWith an eye toward the the article looks at new developments in algorithm design,<br/>hardware, and soware, making sure scalability is still crucial fort ackling issues with even bigger<br/>datasets |
| 654 ## - SUBJECT ADDED ENTRY--FACETED TOPICAL TERMS | |
| Subject | <a href="Scalable Algorithms">Scalable Algorithms</a> |
| -- | <a href="Big Data Analytics">Big Data Analytics</a> |
| -- | <a href="Exponential Data Growth">Exponential Data Growth</a> |
| -- | <a href="Large Dataset">Large Dataset</a> |
| -- | <a href="Machine Learning Methods">Machine Learning Methods</a> |
| -- | <a href="Case Studies">Case Studies</a> |
| -- | <a href="Business Strategies">Business Strategies</a> |
| 773 0# - HOST ITEM ENTRY | |
| Host Biblionumber | 131673 |
| Host Itemnumber | 113505 |
| Place, publisher, and date of publication | New Delhi Enriched Publications |
| Title | Journal of Artificial Intelligence and Soft Computing research |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Journal Article |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Location (home branch) | Sublocation or collection (holding branch) | Date acquired | Koha issues (times borrowed) | Piece designation (barcode) | Koha date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | SNDT Juhu | SNDT Juhu | 01/09/2025 | JP881.1 | 01/09/2025 | 01/09/2025 | Journal Article |