arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 0909.2030cs.DBcs.DS
收藏

具有一般函数相关性的合取向量的大小界

Size Bounds for Conjunctive Queries with General Functional Dependencies

Gregory Valiant, Paul Valiant

摘要:

本文扩展了Gottlob,Lee和Valiant(PODS 2009)[GLV]的工作,并考虑了给定任意函数依赖集的数据库D的合取查询Q的结果Q(D)的大小的最坏情况界限。[GLV]中的边界基于查询变量的“着色”。为了将先前的边界扩展到任意函数依赖的设置,我们利用信息论的工具来形式化原始直觉,即所使用的每种颜色表示该变量的一些可能的熵,并通过线性规划来约束最大可能的大小增加,该线性规划寻求最大化查询结果中比输入多多少熵。这个新的视图使我们能够精确地描述具有简单函数依赖关系(键)的合取查询的最坏情况实例的熵结构,为[GLV]的结果提供了新的见解。我们将这些结果扩展到一般函数依赖的情况下,提供最坏情况下的大小增加的上限和下限。我们确定了这些界限的差距和一个中心的开放问题在信息论之间的根本联系。最后,我们表明,虽然上界和下界是由指数大的线性规划,人们可以区分在多项式时间是否查询的结果与任意一组功能依赖可以是任何大于输入数据库。

英文摘要:

This paper extends the work of Gottlob, Lee, and Valiant (PODS 2009)[GLV], and considers worst-case bounds for the size of the result Q(D) of a conjunctive query Q to a database D given an arbitrary set of functional dependencies. The bounds in [GLV] are based on a "coloring" of the query variables. In order to extend the previous bounds to the setting of arbitrary functional dependencies, we leverage tools from information theory to formalize the original intuition that each color used represents some possible entropy of that variable, and bound the maximum possible size increase via a linear program that seeks to maximize how much more entropy is in the result of the query than the input. This new view allows us to precisely characterize the entropy structure of worst-case instances for conjunctive queries with simple functional dependencies (keys), providing new insights into the results of [GLV]. We extend these results to the case of general functional dependencies, providing upper and lower bounds on the worst-case size increase. We identify the fundamental connection between the gap in these bounds and a central open question in information theory. Finally, we show that, while both the upper and lower bounds are given by exponentially large linear programs, one can distinguish in polynomial time whether the result of a query with an arbitrary set of functional dependencies can be any larger than the input database.

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。

↑