Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Approximate join processing over data streams
Proceedings of the 2003 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
PSoup: a system for streaming queries over streaming data
The VLDB Journal — The International Journal on Very Large Data Bases
Characterizing memory requirements for queries over continuous data streams
ACM Transactions on Database Systems (TODS)
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
Load shedding in a data stream manager
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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A data stream management system (DSMS) has to handle high-volume and bursty data streams with large number of continuous queries. When an input rate of any data stream exceeds the system capacity, the DSMS has to shed load by dropping some fraction of unprocessed data items. In this paper, we propose a new load shedding algorithm for continuous queries over data streams. Unlike previous algorithms assuming that all queries are equally important, we consider the priority of each query so that more important queries make more convincing outputs. As a result, the proposed algorithm can support differentiated quality of services by exploiting semantics inherent to applications. We also report the experiment results confirming the benefits of the proposed algorithm.