LAUSR

Radio frequency identification (RFID) enabled retail store management needs workflow optimization to facilitate real-time decision making. In this paper, complex event processing (CEP) based RFID-enabled retail store management is studied, particularly focusing on automated shelf replenishment decisions. We define different types of event queries to describe retailer store workflow action over the RFID data streams on multiple tagging levels (e.g., item level and container level). Non-deterministic finite automata (NFA) based evaluation models are used to detect event patterns. To manage pattern match results in the process of event detection, optimization algorithm is applied in the event model to share event detection results. A simulated RFID-enabled retail store is used to verify the effectiveness of the method, experiment results show that the algorithm is effective and could optimize retail store management workflow.