Showing papers by "Zhenhui Li published in 2014"

PGT: Measuring Mobility Relationship Using Personal, Global and Temporal Factors

Abstract: Rich location data of mobile users collected from smart phones and location-based social networking services enable us to measure the mobility relationship strength based on their interactions in the physical world. A commonly-used measure for such relationship is the frequency of meeting events (i.e., Co-locate at the same time). That is, the more frequently two persons meet, the stronger their mobility relationship is. However, we argue that not all the meeting events are equally important in measuring the mobility relationship and propose to consider personal and global factors to differentiate meeting events. Personal factor models the probability for an individual user to visit a certain location, whereas the global factor models the popularity of a location based on the behavior of general public. In addition, we introduce the temporal factor to further consider the time gaps between meeting events. Accordingly, we propose a unified framework, called PGT, that considers personal, global, and temporal factors to measure the strength of the relationship between two given mobile users. Extensive experiments on real datasets validate our ideas and show that our method significantly outperforms the state-of-the-art methods.

Mining Periodicity from Dynamic and Incomplete Spatiotemporal Data

Abstract: As spatiotemporal data becomes widely available, mining and understanding such data have gained a lot of attention recently. Among all important patterns, periodicity is arguably the most frequently happening one for moving objects. Finding periodic behaviors is essential to understanding the activities of objects, and to predict future movements and detect anomalies in trajectories. However, periodic behaviors in spatiotemporal data could be complicated, involvingmultiple interleaving periods, partial time span, and spatiotemporal noises and outliers. Even worse, due to the limitations of positioning technology or its various kinds of deployments, real movement data is often highly incomplete and sparse. In this chapter, we discuss existing techniques to mine periodic behaviors from spatiotemporal data, with a focus on tackling the aforementioned difficulties risen in real applications. In particular, we first review the traditional time-series method for periodicity detection. Then, a novelmethod specifically designed to mine periodic behaviors in spatiotemporal data, Periodica, is introduced. Periodica proposes to use reference spots to observe movement and detect periodicity from the in-and-out binary sequence. Then, we discuss the important issue of dealing with sparse and incomplete observations in spatiotemporal data, and propose a new general framework Periodo to detect periodicity for temporal events despite such nuisances.We provide experiment results on real movement data to verify the effectiveness of the proposed methods. While these techniques are developed in the context of spatiotemporal data mining, we believe that they are very general and could benefit researchers and practitioners from other related fields.

MoveMine 2.0: mining object relationships from movement data

Abstract: The development in positioning technology has enabled us to collect a huge amount of movement data from moving objects, such as human, animals, and vehicles. The data embed rich information about the relationships among moving objects and have applications in many fields, e.g., in ecological study and human behavioral study. Previously, we have proposed a system MoveMine that integrates several start-of-art movement mining methods. However, it does not include recent methods on relationship pattern mining. Thus, we propose to extend MoveMine to MoveMine 2.0 by adding substantial new methods in mining dynamic relationship patterns. Newly added methods focus on two types of pairwise relationship patterns: (i) attraction/avoidance relationship, and (ii) following pattern. A user-friendly interface is designed to support interactive exploration of the result and provides flexibility in tuning parameters. MoveMine 2.0 is tested on multiple types of real datasets to ensure its practical use. Our system provides useful tools for domain experts to gain insights on real dataset. Meanwhile, it will promote further research in relationship mining from moving objects.

Spatiotemporal Pattern Mining: Algorithms and Applications

Abstract: With the fast development of positioning technology, spatiotemporal data has become widely available nowadays. Mining patterns from spatiotemporal data has many important applications in human mobility understanding, smart transportation, urban planning and ecological studies. In this chapter, we provide an overview of spatiotemporal data mining methods. We classify the patterns into three categories: (1) individual periodic pattern; (2) pairwise movement pattern and (3) aggregative patterns over multiple trajectories. This chapter states the challenges of pattern discovery, reviews the state-of-the-art methods and also discusses the limitations of existing methods.

Mining GPS Data for Trajectory Recommendation

Abstract: The wide use of GPS sensors in smart phones encourages people to record their personal trajectories and share them with others in the Internet. A recommendation service is needed to help people process the large quantity of trajectories and select potentially interesting ones. The GPS trace data is a new format of information and few works focus on building user preference profiles on it. In this work we proposed a trajectory recommendation framework and developed three recommendation methods, namely, Activity-Based Recommendation (ABR), GPS-Based Recommendation (GBR) and Hybrid Recommendation. The ABR recommends trajectories purely relying on activity tags. For GBR, we proposed a generative model to construct user profiles based on GPS traces. The Hybrid recommendation combines the ABR and GBR. We finally conducted extensive experiments to evaluate these proposed solutions and it turned out the hybrid solution displays the best performance.

Who were you talking to: mining interpersonal relationships from cellphone network data

Abstract: People play different roles in various social networks. Even in a single network, people may interact with others based on different roles, and there are various relationships among them. However, current research usually treats all relationships homogeneously (i.e. friendship). In this paper, we try to identify different types of relationship (family, colleague, and social) within social networks. By analyzing a large-scale cellphone network, we gain insights about human mobility patterns. We design three metrics to capture co-location behaviors for cellphone users, taking spatial-temporal factors into consideration. These metrics show that users with different relationships demonstrate significantly different co-locating patterns. With these metrics as features, we adopt supervised approach to classify cellphone user pairs into different relationship categories. Comparing to using network and communication features, co-location metrics demonstrate better performance to fulfill the task of relationship identification.