通过突破Top-k障碍深入挖掘Deep Web数据库
Digging Deeper into Deep Web Databases by Breaking Through the Top-k Barrier
摘要：
大量的网络数据库只能通过专有的类似表单的界面访问，这些界面要求用户通过输入一些属性的所需值来查询系统。由这样的接口强制执行的关键限制是前k个输出约束，即，当存在大量匹配元组时，通常根据专有的排名函数，只有少数（前k个）匹配元组被网站优先选择并返回。由于大多数web数据库所有者将k设置为小值，因此top-k输出约束阻止了许多有趣的第三方（例如，mashup）服务，以避免在真实世界的Web数据库上开发服务。在本文中，我们认为新的问题“深入挖掘”到这样的网络数据库。我们的主要贡献是元算法GetNext，可以检索下一个排名元组从隐藏的Web数据库中只使用Web数据库的限制性接口，而无需任何先验知识的排名功能。然后可以迭代地调用该算法来检索尽可能多的顶级元组。我们开发的原则和有效的算法，是基于生成和执行多个重构查询，并推断出下一个排名元组从他们的返回结果。我们提供了我们的算法的理论分析，以及广泛的实验结果在合成和现实世界的数据库，说明我们的技术的有效性。
英文摘要：
A large number of web databases are only accessible through proprietary form-like interfaces which require users to query the system by entering desired values for a few attributes. A key restriction enforced by such an interface is the top-k output constraint - i.e., when there are a large number of matching tuples, only a few (top-k) of them are preferentially selected and returned by the website, often according to a proprietary ranking function. Since most web database owners set k to be a small value, the top-k output constraint prevents many interesting third-party (e.g., mashup) services from being developed over real-world web databases. In this paper we consider the novel problem of "digging deeper" into such web databases. Our main contribution is the meta-algorithm GetNext that can retrieve the next ranked tuple from the hidden web database using only the restrictive interface of a web database without any prior knowledge of its ranking function. This algorithm can then be called iteratively to retrieve as many top ranked tuples as necessary. We develop principled and efficient algorithms that are based on generating and executing multiple reformulated queries and inferring the next ranked tuple from their returned results. We provide theoretical analysis of our algorithms, as well as extensive experimental results over synthetic and real-world databases that illustrate the effectiveness of our techniques.
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。
