On Discovery of Traveling Companions from Streaming Trajectories

Abstract

The advance of object tracking technologies leads to huge volumes of spatio-temporal data collected in the form of trajectory data stream. In this study, we investigate the problem of discovering object groups that travel together (i.e., traveling companions) from streaming trajectories. Such technique has broad applications in the areas of scientific study, transportation management and military surveillance. To discover traveling companions, the monitoring system should cluster the objects of each snapshot and intersect the clustering results to retrieve moving-together objects. Since both clustering and intersection steps involve high computational overhead, the key issue of companion discovery is to improve the efficiency of algorithms. We propose the models of closed companion candidates and smart intersection to accelerate data processing. A data structure termed traveling buddy is designed to facilitate scalable and flexible companion discovery from streaming trajectories. The traveling buddies are micro-groups of objects that are tightly bound together. By only storing the object relationships rather than their spatial coordinates, the buddies can be dynamically maintained along trajectory stream with low cost. Based on traveling buddies, the system can discover companions without accessing the object details. The proposed methods are evaluated with extensive experiments on both real and synthetic datasets. The buddy-based method is an order of magnitude faster than baselines. It also achieves higher precision and recall in companion discovery.

Mining Representative Movement Patterns through Compression

“…A moving object cluster can be defined as a group of moving objects that are physically closed to each other for at least some number of timestamps. In this context, many recent studies have been defined such as flocks [5], convoy queries [7], closed swarms [10], group patterns [15], gradual trajectory patterns [6], traveling companions [13], gathering patterns [16], etc... Nevertheless, after the extraction, the end user can be overwhelmed by a huge number of movement patterns although only a few of them are useful.…”

Multidimensional Sensor Data Analysis in Cyber-Physical System: An Atypical Cube Approach

“…Hence this pair will not be integrated in one cluster. Then, the system picks the pair of C A and C C , since these two clusters have most of their atypical data in the common areas with similar times, Sim(C A , C C ) = 0.87; thus they should be merged according to (4). The merged result is tagged as cluster C D .…”

“…The Cyber-Physical System (CPS) has been a focused research theme recently due to its wide applications in the areas of traffic monitoring, battlefield surveillance, and sensornetwork-based monitoring [1][2][3][4][5][6]. It is placed on the top of the priority list for federal research investment in the fiscal year report of US president's council of advisors on science and technology [7].…”

Effective Following Patterns Mining Scheme for the Movements of Objects

“…In 2012, Zheng et al [56] put forward mining algorithm based on travelling buddy ,which was on efficiency as there have been significantly improved, but not for the current big data environment to optimize performance. When faced with large data, the algorithm and has some other time-consuming algorithms would be unacceptable.…”