Leveraging data-driven techniques for efficient data mining in cloud computing environments (Record no. 22709)
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| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250424114126.0 |
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| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 25977 |
| Author | Venkataramana, Jaladurgam |
| 245 ## - TITLE STATEMENT | |
| Title | Leveraging data-driven techniques for efficient data mining in cloud computing environments |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.15(2), Oct |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Chennai |
| Name of publisher, distributor, etc. | ICT Academy |
| Year | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 3515-3522p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | The capacity to efficiently use big data and analytics is becoming a<br/>critical differentiator for company growth in today's data-driven<br/>environment. Using important trends, obstacles, and best practices as a<br/>framework, this article investigates how to promote company growth<br/>via the use of big data and analytics. An important issue in cloud<br/>computing is deciding on an acceptable amount and location of data.<br/>Decisions about resource management are based on data aspects and<br/>operations in data-driven infrastructure management (DDIM), a novel<br/>solution to this problem. It is critical to have a unified system that can<br/>manage various forms of big data and the analysis of that data, as well<br/>as common knowledge management functions. The approach stated in<br/>this research is DD-DM-CCE, or Data-Driven Methods for Efficient<br/>Data Mining in Cloud Computing Environments. Improving data<br/>using derived information from maximum frequent correlated pattern<br/>mining is the main focus of the work. By considering the centrality<br/>factor, the DD-DM-CCE method may help choose the best locations to<br/>store data in order to reduce access latency. In order to gain a<br/>competitive edge, this study offers a cloud-based conceptual framework<br/>that can analyze large data in real time and improve decision making.<br/>Efficient big data processing is possible with cloud computing<br/>infrastructures that can store and analyze massive amounts of data, as<br/>this reduces the upfront cost of the massively parallel computer<br/>infrastructure needed for big data analytics. According to simulations<br/>run on cloud computing, the DD-DM-CCE approach does better than<br/>the status quo regarding hit ratio, effective network utilization, and<br/>average response time. According to this study, data mining methods<br/>are valuable and successful in predicting how consumers will utilize<br/>cloud services. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 773 0# - HOST ITEM ENTRY | |
| Place, publisher, and date of publication | Chennai ICT Academy |
| Title | ICTACT Journal on Soft Computing (IJSC) |
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| URL | https://ictactjournals.in/paper/IJSC_Vol_15_Iss_2_Paper_6_3515_3522.pdf |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 24/04/2025 | 2025-0658 | 24/04/2025 | 24/04/2025 | Articles Abstract Database |