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Skyline queries have recently received considerable attention in the searching service. The skyline of a set of n-dimensional points contains the points that are not dominated by any other point on all dimensions. The Skyline queries help users make intelligent decisions over complex data, where different and often conflicting criteria are considered. Many related works have processed skyline on static data or on moving objects in Euclidean space. However, this paper assumes that query points of skyline continuously move in road network. We propose a new method that processes continuous skyline in road network. This method processes a continuous skyline through pre-computed shortest range data of targets. Our experiments show the effectiveness of the proposed mechanism under various settings. Also it will show an excellent performance in real applications.

Cloud computing has recently become a significant technology trend in the IT field. Among the related technologies, desktop virtualization has been applied to various commercial applications since it provides many advantages, such as lower maintenance and operation costs and higher utilization. However, the existing solutions offer a very limited performance for 3D graphics applications. Therefore, we propose a novel method in which rendering commands are not executed at the host server but rather are delivered to the client through the network and are executed by the client's graphics device. This method prominently reduces server overhead and makes it possible to provide a stable service at low cost. The results of various experiments prove that the proposed method outperforms all existing solutions.

Existing methods to process continuous range queries are not scalable. In particular, as the number of continuous range queries on a large number of moving objects becomes larger, their performance degrades significantly. We propose a novel query indexing method called the projected attribute bit (PAB)‐based query index. We project a two‐dimensional continuous range query on each axis to get two one‐dimensional bit lists. Since the queries are transformed to bit lists and query evaluation is performed by bit operations, the storage cost of indexing and query evaluation time are reduced significantly. Through various experiments, we show that our method outperforms the containment‐encoded squares‐based indexing method, which is one of the most recently proposed methods.

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