Answering why-not questions on spatial keyword top-k queries
Answering why-not questions on spatial keyword top-k queries
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DOI：
10.1109/icde.2015.7113291
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发表时间：
2015-04
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影响因子：
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通讯作者：
Lei Chen;Xin Lin;Haibo Hu;Christian S. Jensen;Jianliang Xu
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文献类型：
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作者：
Lei Chen;Xin Lin;Haibo Hu;Christian S. Jensen;Jianliang Xu
大量的地理标记的文本对象在网络上可用。空间关键字top-k查询根据考虑查询位置和查询关键字的排名函数检索具有最佳得分的k个这样的对象。在这种情况下，用户可能想知道为什么某些已知对象意外地从结果中丢失;理解为什么可以帮助用户检索更好的结果。虽然空间关键字查询已被深入研究，没有建议存在如何为用户提供解释，为什么这样的预期对象从结果中丢失。我们提供的技术，允许修订的空间关键字查询，使其结果包括一个或多个所需的，但缺少的对象。在这样做时，我们采用了查询细化的方法，提供了一个基本的算法，减少了问题的二维几何问题。为了提高性能，我们提出了一个基于索引的排名估计算法，修剪候选结果早期。广泛的实验结果提供了深入了解所提出的技术的设计属性，并表明它们是有效的运行时间和I/O成本。
Large volumes of geo-tagged text objects are available on the web. Spatial keyword top-k queries retrieve k such objects with the best score according to a ranking function that takes into account a query location and query keywords. In this setting, users may wonder why some known object is unexpectedly missing from a result; and understanding why may aid users in retrieving better results. While spatial keyword querying has been studied intensively, no proposals exist for how to offer users explanations of why such expected objects are missing from results. We provide techniques that allow the revision of spatial keyword queries such that their results include one or more desired, but missing objects. In doing so, we adopt a query refinement approach to provide a basic algorithm that reduces the problem to a two-dimensional geometrical problem. To improve performance, we propose an index-based ranking estimation algorithm that prunes candidate results early. Extensive experimental results offer insight into design properties of the proposed techniques and suggest that they are efficient in terms of both running time and I/O cost.