Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Sampling from a moving window over streaming data
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Fjording the Stream: An Architecture for Queries Over Streaming Sensor Data
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
StreaMon: an adaptive engine for stream query processing
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Flexible time management in data stream systems
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Optimizing in-order execution of continuous queries over streamed sensor data
SSDBM'2005 Proceedings of the 17th international conference on Scientific and statistical database management
The CQL continuous query language: semantic foundations and query execution
The VLDB Journal — The International Journal 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
Processing sliding window multi-joins in continuous queries over data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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Stream applications such as sensor data processing, financial tickers and Internet traffic analysis require that information, naturally, occur as a stream of data values. Due to a late and out-of-order arrival of infinite, unbound and multiple input streams, processing continuous queries over them may lead to producing an incorrect answer or delaying query execution. Hence to minimize this waiting time, previous works have used timeout technique without considering the frequency of timeouts. It results in decreasing the accuracy of query execution results, since the more the frequency of timeouts, the more the loss of data. We propose an AP-STO method using StB that stores operator's state and a window time-out method based on the waiting time for the next tuple by resetting the size of a window according to the frequency of timeouts. It reduces a data lost rate and increases the tuples output-rate. We compare AP-STO method with an existing method and use output-rate and response time as criteria for performance evaluation. Our proposed method shows a substantial improvement in system performance in terms of the accuracy of query execution and the increment of tuples output-rate per a query due to the reduction in loss rate of data.