Abstract
With the quick development of RFID technology and the decreasing prices of RFID devices, RFID is becoming widely used in various intelligent services. Especially in the retail application domain, RFID is increasingly adopted to capture the shopping tracks and behavior of in-store customers. To further enhance the potential of this promising application, in this paper, we propose a unified framework for RFID-based path analytics, which uses both in-store shopping paths and RFID-based purchasing data to mine actionable navigation patterns. Four modules of this framework are discussed, which are: (1) mapping from the physical space to the cyber space, (2) data preprocessing, (3) pattern mining and (4) knowledge understanding and utilization. In the data preprocessing module, the critical problem of how to capture the mainstream shopping path sequences while wiping out unnecessary redundant and repeated details is addressed in detail. To solve this problem, two types of redundant patterns, i.e., loop repeat pattern and palindrome-contained pattern are recognized and the corresponding processing algorithms are proposed. The experimental results show that the redundant pattern filtering functions are effective and scalable. Overall, this work builds a bridge between indoor positioning and advanced data mining technologies, and provides a feasible way to study customers’ shopping behaviors via multi-source RFID data.
Highlights
Nowadays, various types of sensors, such as Kinect sensor [1], inertial motion units [2], ultrasound range sensor [2], GPS [3], Radio Frequency Identification (RFID), laser scanners [4] and remote sensor networks [5], have been used to perceive the physical environment, and many promising solutions [2,6,7] have been proposed to realize the effective mapping from the physical world to the cyberspace
We use the retail industry as an example to explore the potential of RFID technology for indoor mapping and navigation
RFID provides the ability to interact with items without physical contact
Summary
Various types of sensors, such as Kinect sensor [1], inertial motion units [2], ultrasound range sensor [2], GPS [3], Radio Frequency Identification (RFID), laser scanners [4] and remote sensor networks [5], have been used to perceive the physical environment, and many promising solutions [2,6,7] have been proposed to realize the effective mapping from the physical world to the cyberspace. In the grocery industry, when various items, returnable transport carts, trolleys and kegs, and valuable products, are equipped with RFID tags, item-level RFID infrastructures are established [9]. They can be utilized to realize a wide range of smart applications, e.g., auto check-outs [9], item-level valuable merchandise tracking [10], vendor managed inventory [11], smart price tags [9], etc. Besides that, shopping carts/trolleys/keys featuring RFID readers can recognize valuable products put into the carts/trolleys/keys, if each valuable product is tagged with an RFID label. Both the walking trajectories of customers and the corresponding purchase behaviors are automatically recorded in the RFID datasets, which are quite precious for mining in-depth knowledge about the shopping behaviors of customers
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