Rule-based multi-query optimization

Data stream management systems usually have to process many long-running queries that are active at the same time. Multiple queries can be evaluated more efficiently together than independently, because it is often possible to share state and computation. Motivated by this observation, various Multi-Query Optimization (MQO) techniques have been proposed. However, these approaches suffer from two limitations. First, they focus on very specialized workloads. Second, integrating MQO techniques for CQL-style stream engines and those for event pattern detection engines is even harder, as the processing models of these two types of stream engines are radically different.

In this paper, we propose a rule-based MQO framework. This framework incorporates a set of new abstractions, extending their counterparts, physical operators, transformation rules, and streams, in a traditional RDBMS or stream processing system. Within this framework, we can integrate new and existing MQO techniques through the use of transformation rules. This allows us to build an expressive and scalable stream system. Just as relational optimizers are crucial for the success of RDBMSes, a powerful multi-query optimizer is needed for data stream processing. This work lays the foundation for such a multi-query optimizer, creating opportunities for future research. We experimentally demonstrate the efficacy of our approach.