Pursuit Estimator Learning Automata Based Approach for Online Event Pattern Tracking

Detecting spatiotemporal pattern from noisy sequences of events plays a very important role in presence sharing, Internet of Things (IoT) and many other fields. As pointed out in existing literature, the core activities of these applications involve event notifications. However, excessive number of event notifications will lead to user's intolerability. Existing literature proposed a Spatiotemporal Pattern Learning Automata (STPLA) to solve this problem effectively. This paper proposed DPri based approach, named as Spatiotemporal DPri (STP-DPri), for online tracking of event pattern. Furthermore, we also show that the STP-DPri is more robustness to noise which is, of course, extremely important in practice.

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