Multiresolution Signal Decomposition: Transforms, Subbands, and Wavelets
Multiresolution Signal Decomposition: Transforms, Subbands, and Wavelets
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
Online outlier detection in sensor data using non-parametric models
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Anomaly detection in information streams without prior domain knowledge
IBM Journal of Research and Development
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Monitoring multimodal data generated by sensor networks for extracting information is a challenging task for the human observer. To manage the barrage of data, one needs to create mechanisms for identifying only those time intervals which are informative and worthy of further high-level analysis either by machine or the human observer. We regard a time interval to be informative and contain an event if it is uncommon or distinct from routine background. Different events in general may unfold at different temporal scales. Here, we present a non-parametric distribution based approach for event detection in sensor network data. In this approach we employ multiple sliding windows at different scales to obtain the distribution of the data. We segment the temporal data stream and identify the potential event bearing candidates by comparing the present and past statistical behavior of the data. In the experiments we demonstrate the effect of optimum bin width selection on accuracy and the range of allowable window sizes and therefore time scales. We analyze the computational speed as well as the supporting empirical results on the bin width.