A Study on Knowledge Mining Methods based on Data Warehouse

DOI:10.21742/ijhit.2653-309X.2021.1.1.01

AUTHORS

Muhieddine Tattersall,Griffith University, Brisbane, Australia

ABSTRACT

With the improvement of users' demand for knowledge, more and more people are not satisfied with the knowledge obtained in the past. To cope with this trend, the main problem of this paper focuses on improving the efficiency of knowledge discovery, especially with the rapid development of database technology, various data warehouses storing complex data types put forward a more severe test for database knowledge discovery. This paper mainly puts forward the problem of database knowledge discovery strategy based on data warehouse, namely algorithm scalability strategy and process-driven strategy, and puts forward a new knowledge discovery model based on these two strategies. The main difficulty of this paper is that this paper focuses on solving the strategy optimization problem of knowledge discovery in general data mining process under the environment of data warehouse, improving the efficiency of data mining, and enabling data and information to be quickly transformed into knowledge that can be used by users and supported by decision. And put forward the strategy of choosing different data mining algorithms for different data. This selection strategy can become an effective bridge between knowledge discovery and mining algorithms based on data warehouse, and both experts and beginners can effectively control the data mining algorithms for knowledge discovery in the data warehouse.