Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Near-optimal sparse fourier representations via sampling
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Efficient Similarity Search in Streaming Time Sequences
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Using multiple windows to track concept drift
Intelligent Data Analysis
Adaptive, hands-off stream mining
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
StreamMiner: a classifier ensemble-based engine to mine concept-drifting data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Fast Discovery of Group Lag Correlations in Streams
ACM Transactions on Knowledge Discovery from Data (TKDD)
Quality-driven resource-adaptive data stream mining?
ACM SIGKDD Explorations Newsletter
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Streaming environments typically dictate incomplete or approximate algorithm execution, in order to cope with sudden surges in the data rate. Such limitations are even more accentuated in mobile environments (such as sensor networks) where computational and memory resources are typically limited. This paper introduces the first “resource adaptive” algorithm for periodicity estimation on a continuous stream of data. Our formulation is based on the derivation of a closed-form incremental computation of the spectrum, augmented by an intelligent load-shedding scheme that can adapt to available CPU resources. Our experiments indicate that the proposed technique can be a viable and resource efficient solution for real-time spectrum estimation.