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Related Experiment Videos

Research on massive information query and intelligent analysis method in a complex large-scale system.

Dai Lin Wang¹, Yun Lei Lv¹, Dan Ting Ren¹

¹Northeast Forestry University, Harbin, 150040, China.

Mathematical Biosciences and Engineering : MBE

|May 30, 2019

Summary

This summary is machine-generated.

This study introduces a novel method for querying and analyzing massive unstructured data, using resume information as a case study. The approach enhances data extraction accuracy and improves retrieval efficiency in large-scale complex systems.

Keywords:

HBase based distributed storage extraction rule model information intelligence system unstructured information

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Area of Science:

Computer Science
Data Science
Information Management

Background:

The exponential growth of big data necessitates advanced methods for information query and intelligent analysis.
Traditional document management techniques are insufficient for handling unstructured massive data in complex systems.
Efficiently managing and retrieving information from large datasets is crucial for modern applications.

Purpose of the Study:

To propose a method for standardized storage, effective extraction, and database construction of massive resume data.
To address the limitations of existing methods in information management and rapid retrieval.
To enable intelligent analysis and retrieval of massive information within large-scale complex systems.

Main Methods:

Utilizing the semi-structured features of resume documents to construct data extraction rule models.
Employing HBase distributed storage for efficient data management.
Leveraging parallel computing technology to optimize storage and query efficiency.

Main Results:

Significantly improved extraction accuracy and recall rate for resume information data.
Demonstrated substantial improvements in massive information retrieval methods compared to traditional approaches.
Showcased enhanced query usage efficiency and intelligent analysis capabilities for complex systems.

Conclusions:

The proposed method effectively handles massive unstructured data, particularly resume information.
The integration of HBase and parallel computing optimizes performance for large-scale data analysis.
This approach offers a robust solution for intelligent information query and analysis in complex systems.