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Database Oriented Big Data Analysis Engine Based on Deep Learning.

Xiaoran Shang¹

¹Software School of Liaoning Technical University, Huludao, Liaoning 125105, China.

Computational Intelligence and Neuroscience

|September 12, 2022

Summary

This summary is machine-generated.

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This study introduces a novel cloud database analysis engine using deep learning and a wolf pack greedy algorithm. The new system significantly enhances data retrieval accuracy and analysis efficiency compared to traditional methods.

Area of Science:

Computer Science
Data Science
Artificial Intelligence

Background:

Growing demand for efficient cloud database solutions in enterprise internet development.
Need for improved data structure and rapid data capture within database analysis engines.
Limitations of traditional character-search-centric data analysis systems.

Purpose of the Study:

To develop an accurate and rapid cloud database analysis engine.
To enhance data structure and analysis efficiency at all database levels.
To leverage big data analysis engine technology and deep learning for cloud databases.

Main Methods:

Construction of a big data analysis engine system utilizing a deep learning model.
Integration of a wolf pack greedy algorithm within the deep learning strategy.

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Analysis of deep learning, wolf greedy algorithm, and data analysis strategies within the engine.

Main Results:

The developed system demonstrates faster response speeds and increased intelligence over traditional engines.
The cloud database analysis engine achieves accurate construction and rapid analysis of data.
Comparative tests show superior performance against character-search-based systems.

Conclusions:

The proposed engine system significantly improves analysis engine effectiveness.
Retrieval accuracy and analysis efficiency for fixed-point data in databases are greatly enhanced.
The integration of deep learning and wolf swarm algorithms offers a powerful solution for cloud database analysis.