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  • 标题:A Superior Approach for Representing Spatial Data in Handheld Devices
  • 本地全文:下载
  • 作者:CH. Swathi ; P. S. G. Aruna Sri
  • 期刊名称:International Journal of Computer Science & Technology
  • 印刷版ISSN:2229-4333
  • 电子版ISSN:0976-8491
  • 出版年度:2012
  • 卷号:3
  • 期号:2
  • 页码:1144-1149
  • 语种:English
  • 出版社:Ayushmaan Technologies
  • 摘要:The spatiotemporal database systems requires the definition of suitable datasets simulating the typical behaviour of moving objects. Previous approaches for generating spatiotemporal data do not consider that moving objects often follow a given network. With the advance of wireless communication technology, it is quite common for people to view maps or get related services from the handheld devices, such as mobile phones and PDAs. Range queries, as one of the most commonly used tools, are often posed by the users to retrieve needful information from a spatial database. However, due to the limits of communication bandwidth and hardware power of handheld devices, displaying all the results of a range query on a handheld device is neither communication efficient nor informative to the users. This is simply because that there are often too many results returned from a range query. In view of this problem, we present a novel idea that a concise representation of a specified size for the range query results, while incurring minimal information loss, shall be computed and returned to the user. Such a concise range query not only reduces communication costs, but also offers better usability to the users, providing an opportunity for interactive exploration. The usefulness of the concise range queries is confirmed by comparing it with other possible alternatives, such as sampling and clustering. In one dimension, a simple dynamic programming algorithm finds the optimal solution in polynomial time. However, this problem becomes NP-hard in two dimensions. Then, we settle for efficient heuristic algorithms for the problem for two or higher dimensions. Our Proposed techniques effectiveness and efficiency examined on real-world data.
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