Abstract:
To solve the problems of traditional materialized view selection(MVS) algorithms, such as the dimensional disaster in the ultra-high dimension caused by the only evaluation index(only evaluate the materialization time, excessive pursuit of the query hit rate of the materialized view), and the shake of the materialized view set, a multi-dimensional big data model optimization algorithm (MMO) based on the weighted graph is proposed.New evaluation indexes are introduced: average query latency and expansion rate.The optimal solution of the materialized view set based on the weighted graph model is found out. Experimental results show that the algorithm in this paper is better than the PSO algorithm in terms of comprehensive score, average query delay, and expansion rate.The dimensional disaster problem under ultra-high dimensional data is solved, and quickly converge.