An in-network reduction algorithm for real-time wireless sensor network applications
Proceedings of the 4th ACM workshop on Wireless multimedia networking and performance modeling
Semantics and implementation of continuous sliding window queries over data streams
ACM Transactions on Database Systems (TODS)
A wavelet-based sampling algorithm for wireless sensor networks applications
Proceedings of the 2010 ACM Symposium on Applied Computing
RoSeS: a continuous content-based query engine for RSS feeds
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
RoSeS: a continuous query processor for large-scale RSS filtering and aggregation
Proceedings of the 20th ACM international conference on Information and knowledge management
Black-box determination of cost models' parameters for federated stream-processing systems
Proceedings of the 15th Symposium on International Database Engineering & Applications
Measuring performance of complex event processing systems
TPCTC'11 Proceedings of the Third TPC Technology conference on Topics in Performance Evaluation, Measurement and Characterization
Research issues in outlier detection for data streams
ACM SIGKDD Explorations Newsletter
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Data stream management systems need to control their resources adaptively since stream characteristics as well as query workload vary over time. In this paper we investigate an approach to adaptive resource management for continuous sliding window queries that adjusts window sizes and time granularities to keep resource usage within bounds. These two novel techniques differ from standard load shedding approaches based on sampling as they ensure exact query answers for given user-defined Quality of Service specifications, even under query re-optimization. In order to quantify the effects of both techniques on the various operations in a query plan, we develop an appropriate cost model for estimating operator resource allocation in terms of memory usage and processing costs. A thorough experimental study not only validates the accuracy of our cost model but also demonstrates the efficacy and scalability of the proposed techniques.