Author(s)

Boyang Liu ¹

Affiliation(s)

¹ Operation Department, ONUS Global Fulfillment Solutions, 7419 Nelson Rd Unit 130, Richmond, BC V6W 1G3, British Columbia Province, Canada

Corresponding Author

Boyang Liu

ABSTRACT

With the rapid growth of scientific research data, traditional data processing methods are no longer able to meet the needs of efficient retrieval and analysis. To address this challenge, this study designed and implemented a holographic retrieval and analysis system for scientific research data based on SSH framework and Lucene engine. The system relies on Oracle data warehouse and combines OLAP technology to achieve multi-dimensional data analysis and display; By using the Lucene full-text search engine, the efficiency and accuracy of data queries have been improved; And with the help of Mahout data mining framework, multiple algorithms are integrated to support deep mining of scientific research data. This study first analyzed the shortcomings of existing decision support systems and identified the core requirements of scientific research management systems. With the support of the SSH framework, the system has achieved efficient data storage, retrieval, analysis, and visualization, forming a complete scientific research data management and analysis solution. After testing, the system has shown high accuracy and stability. The research results indicate that the system significantly improves the efficiency and decision support capability of scientific research management. The development model based on open source technology not only reduces costs, but also enhances the scalability and maintainability of the system, with broad application prospects.

KEYWORDS

Scientific Research Data Management, Full-Text Search, Decision Support System, SSH Framework, Lucene Engine

CITE THIS PAPER

Boyang Liu, Research on Holographic Retrieval and Analysis System for Scientific Research Data Based on SSH Framework and Lucene Engine. Journal of Artificial Intelligence Practice (2024) Vol. 7: 164-171. DOI: http://dx.doi.org/10.23977/jaip.2024.070320.

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