Abstract.A location-based service (LBS) provides information based on the location information specified in a query. Nearest-neighbor (NN) search is an important class of queries supported in LBSs. This paper studies energy-conserving air indexes for NN search in a wireless broadcast environment. Linear access requirement of wireless broadcast weakens the performance of existing search algorithms designed for traditional spatial database. In this paper, we propose a new energyconserving index, called grid-partition index, which enables a single linear scan of the index for any NN queries. The idea is to partition the search space for NN queries into grid cells and index all the objects that are potential nearest neighbors of a query point in each grid cell. Three grid partition schemes are proposed for the grid-partition index. Performance of the proposed grid-partition indexes and two representative traditional indexes (enhanced for wireless broadcast) is evaluated using both synthetic and real data. The result shows that the grid-partition index substantially outperforms the traditional indexes.
Abstract. Due to the growing interests in XML security, various access control schemes have been proposed recently. However, little effort has been put forth to facilitate a uniform analysis and comparison of these schemes under the same framework. This paper presents a first attempt toward a flexible framework that can capture the design principles and operations of existing XML access control mechanisms. Under this framework, we observe that most of existing XML access control mechanisms share the same design principle with slightly different orderings of underlying building blocks (i.e., data, query, and access control rule). Furthermore, according to the framework, we identify four plausible approaches to implement XML access controls, namely built-in, view-based, pre-processing and post-processing. Finally, we compare the actual performance of different approaches.
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), GPSBased 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.
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