MindReader: Querying Databases Through Multiple Examples
MindReader: Querying Databases Through Multiple Examples
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发表时间：
1998-08
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通讯作者：
Y. Ishikawa;R. Subramanya;C. Faloutsos
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作者：
Y. Ishikawa;R. Subramanya;C. Faloutsos
用户往往不能很容易地表达他们的查询。例如，在按内容设置的多媒体/图像中，用户可能想要有日落的照片;在当前的系统中，如QBIC，用户必须给出示例查询，并指定颜色，形状和纹理的相对重要性。更糟糕的是，用户可能想要属性之间的相关性，例如，在传统的医疗记录数据库中，医学研究人员可能想要找到“轻度超重的患者”，其中隐含的查询将是“体重/身高M 4磅/英寸”。我们的目标是提供一个用户友好的，但理论上坚实的方法，来处理这样的查询。我们允许用户给出几个例子，并可选地，他们的“善良”的分数，我们提出了一种新的方法来“猜测”哪些属性是重要的，哪些相关性是重要的，并与什么样的权重。我们的贡献是双重的：（a）我们将问题形式化为最小化问题，并展示了如何求解最优解，完全避免了ad-hoc问题。$这项工作得到了NSF IRI-9625428的支持。此外，美国国家科学基金会、ARPA和NASA根据NSF合作协议编号IRI-9411299进行了研究。允许免费复制本材料的所有OT部分，前提是副本不为直接商业利益而分发，VLDB版权声明和出版物的标题及其日期出现，并通知复制是由超大型数据库基金会许可的。复制，否则，OT再版，需要费用和/或特别许可jrom捐赠。第24届VLDB会议论文集，纽约，美国，1998年。(b)此外，我们是第一个可以处理“对角线”查询（如上面的“超重”查询）的公司。在合成数据集和真实的数据集上的实验表明，该方法能够快速准确地估计出用户心中的“隐藏”距离函数。
Users often can not easily express their queries. For example, in a multimedia/image by content setting, the user might want photographs with sunsets; in current systems, like QBIC, the user has to give a sample query, and to specify the relative importance of color, shape and texture. Even worse, the user might want correlations between attributes, like, for example, in a traditional, medical record database, a medical researcher might want to find “mildly overweight patients”, where the implied query would be “weight/height M 4 lb/inch”. Our goal is to provide a user-friendly, but theoretically solid method, to handle such queries. We allow the user to give several examples, and, optionally, their ‘goodness’ scores, and we propose a novel method to “guess” which attributes are important, which correlations are important, and with what weight. Our contributions are twofold: (a) we formalize the problem as a minimization problem and show how to solve for the optimal solution, completely avoiding the ad-hoc heurist Part of this work was done while this author was vising University of Maryland and Carnegie Mellon University. $ This work was supported by NSF IRI-9625428. Also, by the National Science Foundation, ARPA and NASA under NSF Cooperative Agreement No. IRI-9411299. Permission to copy without fee all OT part of this material is granted provided that the copies are not made OT distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, OT to republish, requires a fee and/or special permission jrom the Endowment. Proceedings of the 24th VLDB Conference New York, USA, 1998 tics of the past. (b) Moreover, we are the first that can handle ‘diagonal’ queries (like the ‘overweight’ query above). Experiments on synthetic and real datasets show that our method estimates quickly and accurately the ‘hidden’ distance function in the user’s mind.