A High Performance Computing Web Search Engine Based on Big Data and Parallel Distributed Models
-д хадгалсан:
| -д хэвлэсэн: | Informatica vol. 48, no. 20 (Dec 2024), p. 27 |
|---|---|
| Үндсэн зохиолч: | |
| Хэвлэсэн: |
Slovenian Society Informatika / Slovensko drustvo Informatika
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| Нөхцлүүд: | |
| Онлайн хандалт: | Citation/Abstract Full Text Full Text - PDF |
| Шошгууд: |
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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MARC
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|---|---|---|---|
| 001 | 3163253355 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 0350-5596 | ||
| 022 | |a 1854-3871 | ||
| 024 | 7 | |a 10.31449/inf.v48i20.6776 |2 doi | |
| 035 | |a 3163253355 | ||
| 045 | 2 | |b d20241201 |b d20241231 | |
| 084 | |a 179436 |2 nlm | ||
| 100 | 1 | |a Ma, Jun |u School of Information Engineering, Changsha Medical University Changsha 410219, China | |
| 245 | 1 | |a A High Performance Computing Web Search Engine Based on Big Data and Parallel Distributed Models | |
| 260 | |b Slovenian Society Informatika / Slovensko drustvo Informatika |c Dec 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This paper presents a high-performance web search system leveraging big data technology. Utilizing a heterogeneous architecture and a parallel distributed computing model based on the MapReduce framework, the system significantly enhances efficiency, scalability, and reliability. The design includes a storage management scheme that integrates cloud storage and grid computing technologies, facilitating efficient storage and rapid access to large-scale data. Key components such as an inverted index structure, vector space model, and semantic analysis models are employed to implement functionalities across the data, logic, and display layers. An experimental environment was set up on the Microsoft Azure cloud platform using the Common Crawl dataset for testing. Performance evaluation, based on metrics including response time, accuracy, and stability, demonstrates the system's superior performance compared to two existing systems, thereby validating its effectiveness. | |
| 653 | |a Big Data | ||
| 653 | |a Machine learning | ||
| 653 | |a Search engines | ||
| 653 | |a Computers | ||
| 653 | |a User needs | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Data structures | ||
| 653 | |a Cloud computing | ||
| 653 | |a Computational grids | ||
| 653 | |a Data search | ||
| 653 | |a System effectiveness | ||
| 653 | |a Data storage | ||
| 653 | |a High performance computing | ||
| 653 | |a Distributed processing | ||
| 653 | |a Efficiency | ||
| 653 | |a Information retrieval | ||
| 653 | |a Vector spaces | ||
| 773 | 0 | |t Informatica |g vol. 48, no. 20 (Dec 2024), p. 27 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3163253355/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3163253355/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3163253355/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |