Open AccessResearch on Resource Allocation Optimization of Information Management System Based on Big data Association Mining
Author(s) -
Yuyong Chen
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 - 10.1088/1742-6596/1345/2/022073
Subject(s) - computer science , cloud computing , human resource management system , association rule learning , resource allocation , resource management (computing) , resource management system , structure of management information , big data , adaptive optimization , management information systems , association (psychology) , information management , database , scheduling (production processes) , information system , data mining , distributed computing , knowledge management , human resource management , engineering , computer security , operations management , philosophy , computer network , operating system , epistemology , network management station , electrical engineering , network architecture , network management application
Aiming at the problem that the resource allocation accuracy of information management system in cloud computing environment is not high, the resource allocation method of information management system in cloud computing environment is improved and designed. A resource allocation algorithm of information management system is proposed based on big data association mining. The information of resource distribution in information management system is fused and reconstructed according to the key words and semantic association features, and then the feature extraction of association rules of information management system resources in cloud computing environment is carried out. The extracted association rules are used for big data training set to allocate the resources of information management system, and big data association mining and adaptive scheduling method are used for adaptive optimization control of resource allocation. The simulation results show that this method can improve the resource allocation accuracy of information management system, the anti-interference ability of resource allocation, and the efficiency of resource utilization and information management.