Next Article in Journal
A Portable Automatic Endpoint Detection System for Amplicons of Loop Mediated Isothermal Amplification on Microfluidic Compact Disk Platform
Next Article in Special Issue
Location Detection and Tracking of Moving Targets by a 2D IR-UWB Radar System
Previous Article in Journal
Hydrodynamic Voltammetry as a Rapid and Simple Method for Evaluating Soil Enzyme Activities
Previous Article in Special Issue
Fast Fingerprint Database Maintenance for Indoor Positioning Based on UGV SLAM
Open AccessArticle

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
Sensors 2015, 15(3), 5344-5375; https://doi.org/10.3390/s150305344
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)
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
Show Figures

Figure 1

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.

Show more citation formats Show less citations formats

Article Access Map by Country/Region

1
Only visits after 24 November 2015 are recorded.
Back to TopTop