원문정보
초록
영어
As the rapid development of IOT (the Internet of Things), RFID technology has been widely applied, and it generates a large of RFID trajectory stream data with the spatial-temporal characteristic. Because RFID has many characteristics, it leads to become very difficult that extracting moving objects groups that together moving (ie. traveling partners) in a period of time from RFID trajectory stream data. Existing methods are difficult to efficiently find this model. This paper presents a closed clustering and intersecting algorithm (CCI) for RFID data to detect movement along traveling partners, which is mainly constituted by two steps: first step is clustering sub-trajectory, it generates sub-trajectory clusters; second step is intersecting sub-trajectories with the traveling partners’ candidate set to improve the candidate set, and find out traveling partners. In this process, we use the principle of Closure to accelerate our processing. Through experiments on the RFID synthetic dataset, we demonstrate the effectiveness and efficiency of our algorithm, thus show that our algorithm is suitable for discovering traveling partners in RFID applications.
목차
1. Introduction
2. Sub Trajectory Clustering Algorithm
2.1. Sub Trajectory Distance Metric
2.2. Sub Trajectory Clustering Based on Density
2.3 The Algorithm Description
3. Travel Companion Generating Algorithm
3.1. The Related Definition and Concepts
3.2. Detailed Description of the Algorithm
4. Experiment and Result Analysis
4.1. Experimental Equipment
4.2. Analysis of Effectiveness
4.3. Efficiency Analysis
5. Conclusion
Acknowledgements
References