Clustering-Based Dynamic Materialized View Selection Algorithm

摘要

With the base of proposing materialized view similarity function, the paper proposes clustering-based dynamic materialized view selection algorithm. It firstly clusters materialized views, and then dynamically adjusts materialized view set. So, it eliminates the "jitter", which dynamic materialized view selection algorithm generally has. The experimental results show that the algorithm not only improves the overall query response performance, but also reduces the computational cost which will bespent during updating materialized view.