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A High Performance Computing Web Search Engine Based on Big Data and Parallel Distributed Models

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Publicado en:Informatica vol. 48, no. 20 (Dec 2024), p. 27
Autor Principal: Ma, Jun
Publicado:
Slovenian Society Informatika / Slovensko drustvo Informatika
Materias:
Big Data
Machine learning
Search engines
Computers
User needs
Performance evaluation
Artificial intelligence
Data structures
Cloud computing
Computational grids
Data search
System effectiveness
Data storage
High performance computing
Distributed processing
Efficiency
Information retrieval
Vector spaces
Acceso en liña:Citation/Abstract
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Resumo: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.
ISSN:0350-5596
1854-3871
DOI:10.31449/inf.v48i20.6776
Fonte:Advanced Technologies & Aerospace Database