Continuous queries over append-only databases
SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
Eddies: continuously adaptive query processing
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
NiagaraCQ: a scalable continuous query system for Internet databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Continuously adaptive continuous queries over streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Tribeca: A Stream Database Manager for Network Traffic Analysis
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Issues in data stream management
ACM SIGMOD Record
Operator scheduling in data stream systems
The VLDB Journal — The International Journal on Very Large Data Bases
Real-Time Scheduling for Data Stream Management Systems
ECRTS '05 Proceedings of the 17th Euromicro Conference on Real-Time Systems
Preemptive rate-based operator scheduling in a data stream management system
AICCSA '05 Proceedings of the ACS/IEEE 2005 International Conference on Computer Systems and Applications
Streaming queries over streaming data
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Monitoring streams: a new class of data management applications
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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More and more applications involve processing continuous data streams, and the data stream management system (DSMS) is designed to deal with such data streams. Due to features of large volume and stochastic arrival, DSMS must process data stream efficiently in order to avoid system memory exhaustion and reduce the data access latency, to satisfy requirements of the application requirement. One of the key factors, which significantly impact the system performance significantly, is the scheduling strategy adopted by the DSMS. Chain scheduling is an operator-based scheduling strategy for DSMS, which has near-optimal in terms of run-time memory usage. FIFO strategy achieves optimal performance in terms of data access latency. Inspired by the two important scheduling strategies, Chain and FIFO, we propose two novel adaptive strategies for DSMS, ASCF and CSS, which efficiently deal with the varying input load in terms of both memory usage and data access latency. To give a fair comparison performance with other competing strategies, we design thorough simulation experiment and run different strategies under the same system environment. The outcomes of simulation experiment demonstrate the potential benefits and advantages of ASCF and CSC.