Towards Efficient Data Sampling for Temporal Anomaly Detection in Sensor Networks Data streams sampling is intended to build a sample on which the future data analysis tasks will be performed. Several parameters affect the effectiveness of the built sample: the used sampling algorithm, chosen sampling rate, and window size if the sliding window model is adopted. Thus, given a stream of items, the most challenging task is to select the most relevant sampling technique to apply and the right parameters to employ to sample the data. In this paper, we address the impact of data sampling on the anomaly detection results. First, we develop a new version of the Weighted Random Sampling (WRS) algorithm that samples the data based on their values with respect to the values of their neighbors in the current sliding window. Thereafter, we study the impact of the sampling process on the anomalies detection using the Exponential Weighted Moving Average (EWMA) algorithm. In this context, the comparison of the sampling algorithms is based on their response time in case of anomaly and the relevance of the detected anomalies.

A streaming approach to reveal crowded events from cellular data Abstract   Anomaly detection has been a very popular research topics over the last few years and applies to many scenarios from different disciplines. This research focuses on crowded phenomena, and addresses the detection of popular events by looking for xe2x80x9canomalousxe2x80x9d patterns in cellular traffic data. In particular, the paper elaborates upon previous proposals and presents two streaming algorithms based on the wavelet decomposition of traffic data. The new algorithms consume traffic samples as soon as they are available, elaborate the data in real time and possibly raise alarms upon threshold crossing. The effectiveness of the approach is assessed by using the public dataset containing the real cellular data acquired over the network of the most popular Italian traffic operator. The experiments prove that the streaming algorithms generally achieve performance comparable to that of their offline counterparts, and that the small degradation that may occasionally be observed is however well counterbalanced by the obvious advantage of detecting anomalies in realxe2x80x93time with no need to wait for the elaboration of overly long traffic timeseries.