Mining periodic behaviors for moving objects

By Zhenhui Jessie Li, Bolin Ding, Jiawei Han, R. Kays, P. Nye. Knowledge Discovery and Data Mining, 2010

322 citations recorded by the source corpus.

Periodicity is a frequently happening phenomenon for moving objects. Finding periodic behaviors is essential to understanding object movements. However, periodic behaviors could be complicated, involving multiple interleaving periods, partial time span, and spatiotemporal noises and outliers. In this paper, we address the problem of mining periodic behaviors for moving objects. It involves two sub-problems: how to detect the periods in complex movement, and how to mine periodic movement behaviors. Our main assumption is that the observed movement is generated from multiple interleaved periodic behaviors associated with certain reference locations. Based on this assumption, we propose a two-stage algorithm, Periodica, to solve the problem. At the first stage, the notion of observation spot is proposed to capture the reference locations. Through observation spots, multiple periods in the movement can be retrieved using a method that combines Fourier transform and autocorrelation. At the second stage, a probabilistic model is proposed to characterize the periodic behaviors. For a specific period, periodic behaviors are statistically generalized from partial movement sequences through hierarchical clustering. Empirical studies on both synthetic and real data sets demonstrate the effectiveness of our method.

Subfields: Signal Processing

View Paper: https://www.semanticscholar.org/paper/7bd04305faa9563bfc3d7b186e1c90f5c09d2bce

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Signal Processing

1. Yu Zheng
2. Xing Xie
3. Jing Yuan
4. Ling-Yin Wei
5. Jure Leskovec
6. Sanjay Chawla
7. Ouri Wolfson
8. Guangzhong Sun

Citations in the corpus

• Trajectory Data Mining (2015, methodology): “[58] further extend the research [57] to mining periodic patterns from incomplete and sparse data sources.”
• Friendship and mobility: user movement in location-based social networks (2011, background): “One would expect that peopl e exhibit strong periodic behavior in their movement as they mov e back and forth between their homes and workplaces [7, 23].”
• Constructing popular routes from uncertain trajectories (2012, background): “Examples of applications are trip planning [2,5,7,8,15,17], animal movement behavior studies [9] and traffic flow analysis [14].”
• Discovering spatio-temporal causal interactions in traffic data streams (2011, methodology): “Moving objects are usually associated with periodic behavioral patterns, and there have been several methods proposed to address the problem of detecting such periodic movements [3, 4, 10, 9, 16].”