ACM Transactions on Database Systems (TODS)
Extensible/rule based query rewrite optimization in Starburst
SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
Database tuning: a principled approach
Database tuning: a principled approach
NiagaraCQ: a scalable continuous query system for Internet databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Filtering algorithms and implementation for very fast publish/subscribe systems
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Windows NT Performance: Monitoring, Benchmarking, and Tuning
Windows NT Performance: Monitoring, Benchmarking, and Tuning
Continuously adaptive continuous queries over streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Access path selection in a relational database management system
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
Composite Event Specification in Active Databases: Model & Implementation
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Composite Events for Active Databases: Semantics, Contexts and Detection
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Multiple aggregations over data streams
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
High-performance complex event processing over streams
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
On-the-fly sharing for streamed aggregation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Monitoring streams: a new class of data management applications
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Scheduling for shared window joins over data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
The case for precision sharing
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Efficient pattern matching over event streams
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Towards expressive publish/subscribe systems
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Streaming multiple aggregations using phantoms
The VLDB Journal — The International Journal on Very Large Data Bases
An algebric window model for data stream management
Proceedings of the Ninth ACM International Workshop on Data Engineering for Wireless and Mobile Access
E-Cube: multi-dimensional event sequence analysis using hierarchical pattern query sharing
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
High-performance composite event monitoring system supporting large numbers of queries and sources
Proceedings of the 5th ACM international conference on Distributed event-based system
Adaptive optimization for multiple continuous queries
Data & Knowledge Engineering
Elastic complex event processing
Proceedings of the 8th Middleware Doctoral Symposium
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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.