Recursive queries have been traditionally studied in the framework of datalog, a language that restricts recursion to monotone queries over sets, which is guaranteed to converge in polynomial time in the size of the input. But modern big data systems require recursive computations beyond the Boolean space. In this paper we study the convergence of datalog when it is interpreted over an arbitrary semiring. We consider an ordered semiring, define the semantics of a datalog program as a least fixpoint in this semiring, and study the number of steps required to reach that fixpoint, if ever. We identify algebraic properties of the semiring that correspond to certain convergence properties of datalog programs. Finally, we describe a class of ordered semirings on which one can use the semi-naive evaluation algorithm on any datalog program.
翻译:传统上在数据学的框架内研究递归性查询,这种语言限制单质查询的复发,保证在输入大小的多元时间里聚合。但现代大数据系统需要超越布尔空间的递归性计算。在本文中,当数据学被解读为任意的半环时,我们研究数据学的趋同性。我们把一个定序半环视为一个定序半环,将数据学程序的语义定义为这个半断点中最起码的固定点,并研究达到该固定点所需的步骤数量。我们确定半环的代数属性,这些属性与数据学程序的某些趋同性相对应。最后,我们描述了一组有定序的半环,可以用来对任何数据仪程序使用半惯性评价算法。