Trigger condition testing and view maintenance using optimized discrimination networks

Presents a structure that can be used both for trigger condition testing and view materialization in active databases, along with a study of techniques for optimizing the structure. The structure presented is known as a discrimination network. The type of discrimination network introduced and studied in this paper is a highly general type of discrimination network which we call the Gator network. The structure of several alternative Gator network optimizers is described, along with a discussion of optimizer performance, output quality and accuracy. The optimizers can choose an efficient Gator network for testing the conditions of a set of triggers or optimizing maintenance of a set of views, given information about the structure of the triggers or views, database size, predicate selectivity and update frequency distribution. The efficiency of optimized Gator networks relative to alternatives is analyzed. The results indicate that, overall, Gator networks can be optimized effectively and can give excellent performance for trigger condition testing and materialization of views.