NiagaraCQ: a scalable continuous query system for Internet databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Rate-based query optimization for streaming information sources
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Aurora: a new model and architecture for data stream management
The VLDB Journal — The International Journal on Very Large Data Bases
Load Shedding for Aggregation Queries over Data Streams
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Static optimization of conjunctive queries with sliding windows over infinite streams
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
The CQL continuous query language: semantic foundations and query execution
The VLDB Journal — The International Journal on Very Large Data Bases
State-slice: new paradigm of multi-query optimization of window-based stream queries
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Load shedding in a data stream manager
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A video stream management system for heterogeneous information integration environments
Proceedings of the 2nd international conference on Ubiquitous information management and communication
Efficient probabilistic event stream processing with lineage and Kleene-plus
International Journal of Communication Networks and Distributed Systems
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Because of increased stream data, managing stream data has become quite important. This paper describes our data stream management system, which employs an architecture combining a stream processing engine and DBMS. Based on the architecture, the system processes both continuous queries and traditional one-shot queries. Our proposed query language supports not only filtering, join, and projection over data streams, but also continuous persistence requirements for stream data. Users can also specify continuous queries that integrate streaming data and historical data stored in DBMS. Another contribution of this paper is feasibility validation of queries. Processing queries on streams with frequent inputs may cause the system to overflow its capacity. Specifically, the maximum writing rate to DBMS is a significant bottleneck when we try to store stream data into DBMS. Our system detects infeasible queries in advance.