We present an overview and initial performance assessment of a rule-based query optimizer written in VenusDB. VenusDB is an active-database rule language embedded in C++. Following the developments in extensible database query optimizers, first in rule-based form followed by optimizers written as object-oriented programs, the VenusDB optimizer avails the advantages of both. To date, development of rule-based query optimizers have included the definition and implementation of custom rule languages. Thus, extensibility required detailed understanding and often further development of the underlying search mechanism of the rule system. Objectoriented query optimizers appear to have achieved their goals with respect to a clear organization and encapsulation of an optimizer's elements. They do not, however, provide for the concise, declarative expression of domain specific heuristics. Our experience demonstrates that a rule-based query optimizer developed in VenusDB can be well structured, ...

Machine Interface.............................................................................................. 26 3.2.3 Concurrency Control....................................................................................................... 27 3.3 VENUSDB LANGUAGE SEMANTICS: AN EVALUATION ............................................................ 28 3.3.1 Related Work.................................................................................................................... 30 3.3.2 The Mortgage Pool Allocation Problem .......................................................................... 33 3.3.3 Quantitative Results ......................................................................................................... 37 3.3.4 Discussion and Conclusions ............................................................................................ 43 CHAPTER 4 APPLICATION SEMANTICS FOR ACTIVE LOG MONITORING APPLICATIONS ............................................................................................45 4.1 MOTIVATION ........................................................................................................................... 46 4.1.1 Coupling Modes............................................................................................................... 47 4.1.2 Example 1 ........................................................................................................................ 48 4.2 BACKGROUND.......................................................................................................................... 50 4.2.1 LMAs, Datalog, and Confluence ..................................................................................... 50 4.2.2 Previous Work....................................................