A Parallel Optimistic Dynamic Optimization of Discrimination Networks for Active Databases

Abstract

Abstract An active database system integrates a database manage- ment system with a rule base system. It is capable of handling a lot of enhanced functionality of databases by triggering off appropriate actions ordained by predefined rules for database events such as queries, and updates the database. However, the active database has a disadvantage in per- formance because checking rule conditions are considerably slow due to a large number of comparison among items. Therefore, some kind of mechanism for speeding up the rule checking process is in great demand for improving the overall performance of an active database. Some active databases make use of discrimination networks to improve the speed of checking rule conditions, but it is not still sufficient. In this paper, we propose a parallel query optimization method and its timing modes for a TREAT-like discrim- ination network in our parallel active relational database prototype system, named Parade. The TREAT-like dis- crimination network can be treated as a set of query trees. In this case, the query trees are used repeatedly, and they can be optimized not just for the current condition check but also for the future ones. Thus the cost of the opti- mization can be hidden by parallel execution of the query tree and its corresponding optimization. Although the pro- posed method requires extra-processors for optimization, it makes good use of parallel machines, and can derive an optimal query tree as the state of databases changes. We also show the experimental results of the proposed metho