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|Title:||MOVING OBJECTS MANAGEMENT FOR LOCATION-BASED SERVICES||Authors:||GUO LONG||Keywords:||moving objects, location-based services, moving range queries, dynamic event streams, influence maximization, location-aware advertising||Issue Date:||17-Aug-2015||Citation:||GUO LONG (2015-08-17). MOVING OBJECTS MANAGEMENT FOR LOCATION-BASED SERVICES. ScholarBank@NUS Repository.||Abstract:||With the rapid development of GPS-enabled devices and wireless communication technologies, location-based services have attracted significant attention from both academic and industry communities. In this thesis, we focus on the management of moving objects data to make location-based services more intelligent in order to improve people's quality of life. Many interesting applications that target moving objects have great potential to bring revolutionary changes to people's life. However, there is an urgent call for efficient algorithms to support these applications, due to the explosion of geo-tagged information collected from various channels in this era. In this thesis, we have identified problems that are related to moving objects and have many interesting applications in location-based services, and proposed frameworks with efficient algorithms to solve these problems. In particular, this thesis proposes three novel problems that deal with three types of spatial queries, respectively. First, we study the efficient processing of moving spatial keyword queries on road networks. Such queries consider both spatial locations and textual descriptions to find top k best objects of interest. Second, we study the efficient processing of moving spatial queries against dynamic event streams. In this problem, the server continuously monitors moving users subscribing to dynamic event streams, and notifies users instantly when there is a matching event nearby. Third, we study the efficient processing of optimal trajectories queries for influence maximization. Such queries find k best trajectories to be attached with a given advertisement and maximizes the expected influence of the advertisement among a large group of audience. For each framework proposed in the thesis, we conduct extensive experiments in realistic settings with both real and synthetic datasets. These experiments reveal the effectiveness and efficiency of the proposed frameworks.||URI:||http://scholarbank.nus.edu.sg/handle/10635/121952|
|Appears in Collections:||Ph.D Theses (Open)|
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