Open AccessResearch on keyword indexing algorithm based on big data
Author(s) -
Baofeng Hui
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1213/3/032032
Subject(s) - computer science , information retrieval , data retrieval , keyword density , search engine indexing , data mining , data redundancy , human–computer information retrieval , redundancy (engineering) , keyword search , search engine , database , operating system
Keyword retrieval is widely used in various aspects of Web information, such as information processing, data mining, etc. Keyword search has been studied for a long time. Many research results have been obtained from keyword search of early relational database, keyword search of semi-structured data and keyword search of graph data. Given multiple keywords, the goal of keyword retrieval is to find the matching node of keywords and find the most compact data fragment containing all keywords. For traditional retrieval model exists in the retrieval of data redundancy, the disadvantage of fuzzy matching, retrieval information deviation, combined with the current hot, discusses the heterogeneous data integration and redundant data, efficient data classification, keyword retrieval model and the method, such as to big data environment, make full use of the traditional technology combined with spatial retrieval model and other technical storage model, improved classification algorithm, and optimize the retrieval algorithm, thus improve the operation efficiency of the algorithm, to provide users with a set of data storage, classification and retrieval in the integration of large data retrieval platform.