A framework for mining actionable navigation patterns from in-store RFID datasets via indoor mapping

Sensors
Bin ShenLibo Xu

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, ...Continue Reading

References

Mar 23, 2012·Sensors·Kourosh Khoshelham, Sander Oude Elberink

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Citations

May 14, 2016·Sensors·Kourosh Khoshelham, Sisi Zlatanova
Dec 21, 2017·Sensors·Taner Arsan, Orcun Kepez

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Software Mentioned

LANDMARC
SpotON
RFID Path Explorer
Windows

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