Research Article
Analysis and Practice of Automatic Indexing in Big Data
@INPROCEEDINGS{10.4108/eai.15-12-2023.2345391, author={Bo Zhao and Hailin Liao and Zebin Wen and Wanting Wen}, title={Analysis and Practice of Automatic Indexing in Big Data}, proceedings={Proceedings of the 3rd International Conference on Public Management and Big Data Analysis, PMBDA 2023, December 15--17, 2023, Nanjing, China}, publisher={EAI}, proceedings_a={PMBDA}, year={2024}, month={5}, keywords={automatic indexing; big data; database}, doi={10.4108/eai.15-12-2023.2345391} }- Bo Zhao
Hailin Liao
Zebin Wen
Wanting Wen
Year: 2024
Analysis and Practice of Automatic Indexing in Big Data
PMBDA
EAI
DOI: 10.4108/eai.15-12-2023.2345391
Abstract
In the digital age, the explosive growth of data and the need for instant performance pose challenges to database management systems. In a big data environment, query performance has become a key issue, and index optimization is a key means to improve performance. Automatic Indexing It dynamically selects, creates and adjusts indexes through machine learning models, evaluates the need for new indexes and the need for existing indexes, creates a new index when needed, and deletes it when it is no longer needed. This article introduces the working principle and research methods of automatic indexing, conducts simulation experiments, and analyzes the experimental results to complete the application exploration and practice of automatic indexing technology in a big data environment. At the end of the text, the challenges and limitations of automatic indexing are presented.