在吸引和排斥维度的混合上搜索Top-k
Answering Top-k Queries Over a Mixture of Attractive and Repulsive Dimensions
摘要：
在本文中，我们制定了一个top-k查询，比较数据库中的对象，用户提供的查询对象上的一个新的评分函数。建议的评分函数结合了吸引和排斥维度的思想到一个通用的框架，以克服传统的距离或相似性措施的弱点。我们研究了所提出的评分函数类的属性，并开发了高效和可扩展的索引结构，索引等值线的功能。我们演示了各种情况下，查询发现应用程序。经验评估表明，查询时间上的性能增益的一个到两个数量级超过现有的最先进的top-k技术。此外，对真实数据集进行定性分析，以突出所提出的查询在发现隐藏数据特征方面的潜力。
英文摘要：
In this paper, we formulate a top-k query that compares objects in a database to a user-provided query object on a novel scoring function. The proposed scoring function combines the idea of attractive and repulsive dimensions into a general framework to overcome the weakness of traditional distance or similarity measures. We study the properties of the proposed class of scoring functions and develop efficient and scalable index structures that index the isolines of the function. We demonstrate various scenarios where the query finds application. Empirical evaluation demonstrates a performance gain of one to two orders of magnitude on querying time over existing state-of-the-art top-k techniques. Further, a qualitative analysis is performed on a real dataset to highlight the potential of the proposed query in discovering hidden data characteristics.
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。
