Materialized Views in Data Mining

Data mining is an interactive and iterative process. A user defines a set of interesting patterns choosing the dataset to be mined and setting the values of various parameters that drive mining algorithm. It is highly probable that a user will issue the same mining query several times until he receives satisfying results. During each run a user will slightly modify either the definition of the mined dataset or the parameters of the algorithm. Currently available mining algorithms suffer from long processing times depending mainly on the size of the dataset. As the pattern discovery takes place mainly in the data warehouse environment, such long processing times are unacceptable from the point of view of interactive data mining. On the other hand, the results of consecutive data mining queries are very similar. One possible solution is to reuse materialized results of previous data mining queries. In this paper we present the concept of materialized data mining views and we show how the results stored in these views can be used to accelerate processing of data mining queries. We demonstrate the use of materialized views in the domains of association rules discovery and sequential pattern search.