Caching Derived Data in Object-Oriented Databases, and an Intelligent System Design for Selecting Their Materialization Strategies

Abstract

To recompute from scratch, cache, or maintain a running-total is a database optimization question. The best answer depends in part upon usage patterns. For example, is total accessed frequently, or is the underlying table updated frequently? These usage patterns cannot necessarily be accurately predicted during application design. I describe materialization cost estimates based on having the database observe and collect data on actual usage patterns, and the actual costs observed implementing different choices. I designed an intelligent system which dynamically makes materialization choices using these cost estimates. The system is adaptive, and switches between the possible materialization choices as usage patterns change.