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Sensors 2015, 15(3), 5344-5375; doi:10.3390/s150305344

A Framework for Mining Actionable Navigation Patterns from In-Store RFID Datasets via Indoor Mapping

1
Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, China
2
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
*
Author to whom correspondence should be addressed.
Academic Editors: Kourosh Khoshelham and Sisi Zlatanova
Received: 11 December 2014 / Revised: 26 February 2015 / Accepted: 27 February 2015 / Published: 5 March 2015
(This article belongs to the Special Issue Sensors for Indoor Mapping and Navigation)
View Full-Text   |   Download PDF [1284 KB, uploaded 17 March 2015]   |  

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. View Full-Text
Keywords: RFID; indoor mapping; shopping transaction path mining; data preprocessing; filtering redundant patterns; framework RFID; indoor mapping; shopping transaction path mining; data preprocessing; filtering redundant patterns; framework
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Shen, B.; Zheng, Q.; Li, X.; Xu, L. A Framework for Mining Actionable Navigation Patterns from In-Store RFID Datasets via Indoor Mapping. Sensors 2015, 15, 5344-5375.

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