Index for fast retrieval of uncertain spatial point data
Index for fast retrieval of uncertain spatial point data
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DOI：
10.1145/1183471.1183504
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发表时间：
2006-11
期刊：
影响因子：
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通讯作者：
D. Kalashnikov;Y. Ma;S. Mehrotra;Ramaswamy Hariharan
中科院分区：
文献类型：
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作者：
D. Kalashnikov;Y. Ma;S. Mehrotra;Ramaswamy Hariharan
以传感器数据、视频流、人类观察等形式从各种来源收集的位置信息通常是不精确和不确定的，需要近似表示。为了表示这种不确定的位置信息，最近有人提出使用概率模型来捕获不精确的位置作为概率密度函数（pdf）。根据应用程序的类型和不精确的来源，pdf可以任意复杂。因此，有效地表示、存储和查询pdf是一项非常具有挑战性的任务。当前最先进的索引方法将pdf的表示和存储视为黑盒，而在本文中，我们将挑战以有效的方式表示和存储任何复杂的pdf。我们进一步开发了索引此类pdf的技术，以支持位置查询的有效处理。我们的大量实验表明，我们的索引技术明显优于现有的最佳解决方案。
Location information gathered from a variety of sources in the form of sensor data, video streams, human observations, and so on, is often imprecise and uncertain and needs to be represented approximately. To represent such uncertain location information, the use of a probabilistic model that captures the imprecise location as a probability density function (pdf) has been recently proposed. The pdfs can be arbitrarily complex depending on the type of application and the source of imprecision. Hence, efficiently representing, storing and querying pdfs is a very challenging task. While the current state of the art indexing approaches treat the representation and storage of pdfs as a black box, in this paper, we take the challenge of representing and storing any complex pdf in an efficient way. We further develop techniques to index such pdfs to support the efficient processing of location queries. Our extensive experiments demonstrate that our indexing techniques significantly outperform the best existing solutions.