Open AccessResearch on Hybrid Index Method of Double-Layer B+ Tree for Power Big Data Considering Knowledge Graph
Author(s) -
Ling Gao,
Li Yao,
Zhiwei Yang,
Fei Zheng
Publication year - 2021
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 - 10.1088/1742-6596/1771/1/012004
Subject(s) - computer science , data mining , relevance (law) , big data , index (typography) , graph , key (lock) , tree (set theory) , power grid , power (physics) , data access layer , grid , power graph analysis , theoretical computer science , database , data modeling , mathematics , geometry , computer security , quantum mechanics , world wide web , political science , law , mathematical analysis , physics
Efficient power data access can not only ensure the normal operation of power system, but also be one of the key supporting technologies to improve the operational efficiency of power grid enterprises. Aiming at the problem that traditional B+ tree does not take into account the relevance of power information fragments and cannot be indexed flexibly, a hybrid index structure and index method of power big data with knowledge graph is proposed, that is, the first layer uses B+ tree structure to store attributes, and the second layer uses knowledge graph to store attribute value, thus realizing the relevance index of power data. The simulation results show that the hybrid index structure can achieve the index of power big data with relevance information without affecting the index efficiency, which provides technical reference for efficient retrieval of multi-modal data in the future.