不确定性空间数据管理综述
Uncertain Spatial Data Management:An Overview
摘要：
目前的技术趋势，如智能手机，通用移动设备，固定传感器和卫星，以及使用这种技术来自愿共享丰富的位置信息的新的用户心态，产生了大量的地理空间和地理时空数据。这种数据洪流为发现新的有用知识提供了巨大的潜力。但除了测量不精确之外，空间数据通常是在离散观测之间插值的。为了减少通信和带宽利用率，数据通常会进行缩减，从而消除一些已知/记录的值。这些问题在空间数据管理中引入了不确定性的概念，这一方面迫切需要可扩展和灵活的解决方案。本章的主要范围是调查现有的技术管理，查询和挖掘不确定的空间数据。首先，本章概述了不确定数据的常见数据表示，解释了常用的可能世界语义来解释不确定数据库，并概述了现有的系统来处理不确定数据。然后，本章定义了概率结果语义的概念，以区分计算单个对象概率和计算整个结果概率的任务。这一点很重要，因为对于许多查询，计算对象级概率的问题可以有效地解决，而结果级概率很难计算。最后，本章介绍了一种新的范式，以有效地回答任何类型的查询不确定的数据：范式的等效世界，分组的指数集的可能数据库世界到一个多项式数量的集合的等效世界，可以有效地处理。使用不确定范围查询的例子提供查询不确定空间数据的例子和用例。
英文摘要：
Both the current trends in technology such as smartphones, general mobile devices, stationary sensors, and satellites as well as a new user mentality of using this technology to voluntarily share enriched location information produces a flood of geo-spatial and geo-spatiotemporal data. This data flood provides tremendous potential for discovering new and useful knowledge. But in addition to the fact that measurements are imprecise, spatial data is often interpolated between discrete observations. To reduce communication and bandwidth utilization, data is often subjected to a reduction, thereby eliminating some of the known/recorded values. These issues introduce the notion of uncertainty in spatial data management, an aspect raising the imminent need for scalable and flexible solutions. The main scope of this chapter is to survey existing techniques for managing, querying, and mining uncertain spatial data. First, this chapter surveys common data representations for uncertain data, explains the commonly used possible worlds semantics to interpret an uncertain database, and surveys existing system to process uncertain data. Then, this chapter defines the notion of probabilistic result semantics to distinguish the task of computing individual object probabilities versus computing entire result probabilities. This is important, as, for many queries, the problem of computing object-level probabilities can be solved efficiently, whereas result-level probabilities are hard to compute. Finally, this chapter introduces a novel paradigm to efficiently answer any kind of query on uncertain data: the Paradigm of Equivalent Worlds, which groups the exponential set of possible database worlds into a polynomial number of sets of equivalent worlds that can be processed efficiently. Examples and use-cases of querying uncertain spatial data are provided using the example of uncertain range queries.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
