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Anomaly Detection and Reporting System for POS

Sudden failure of point-of-sale (POS) system due to disk and memory faults can cause huge loss. A protective solution to avoid undesirable situation is to monitor POS system for any abnormal behavior. In this study, we proposed a fault prediction system based on deep auto-encoders. We designed a deep auto encoder that uses desktop and mobile POS data and significantly identify the anomaly. In addition, we also introduce a fault reporting strategy that efficiently report faults based on its severity and urgency. The proposed reporting system intelligently operates to mitigate the issues quickly and insure the smooth operation of POS.