BigData通过递归中的聚集实现从图分析到机器学习的应用
BigData Applications from Graph Analytics to Machine Learning by Aggregates in Recursion
摘要：
在过去，聚集的非单调性质所引起的语义问题常常妨碍它们在逻辑程序和演绎数据库的递归语句中的使用。然而，最近引入的概念的预映射（PreM）表明，在关键的应用程序的兴趣，聚集可以用于递归优化的完美模型语义的聚集分层程序。因此，我们可以保留这些程序的声明式形式语义，同时实现高效的操作语义，有利于并行和分布式平台上的可扩展实现。在本文中，我们表明，与PreM，广泛的经典算法的实际利益，范围从图分析和动态规划为基础的优化问题，数据采掘和机器学习应用程序可以简洁地表示在声明性语言中使用聚合递归。我们的例子也被用来表明，PreM可以检查使用简单的技术和模板化的验证策略。现在，各种高级BigData应用程序都可以用基于逻辑的语言（包括Datasheet、Prolog甚至SQL）以声明方式表达，同时使其执行具有卓越的性能和可扩展性。
英文摘要：
In the past, the semantic issues raised by the non-monotonic nature of aggregates often prevented their use in the recursive statements of logic programs and deductive databases. However, the recently introduced notion of Pre-mappability (PreM) has shown that, in key applications of interest, aggregates can be used in recursion to optimize the perfect-model semantics of aggregate-stratified programs. Therefore we can preserve the declarative formal semantics of such programs while achieving a highly efficient operational semantics that is conducive to scalable implementations on parallel and distributed platforms. In this paper, we show that with PreM, a wide spectrum of classical algorithms of practical interest, ranging from graph analytics and dynamic programming based optimization problems to data mining and machine learning applications can be concisely expressed in declarative languages by using aggregates in recursion. Our examples are also used to show that PreM can be checked using simple techniques and templatized verification strategies. A wide range of advanced BigData applications can now be expressed declaratively in logic-based languages, including Datalog, Prolog, and even SQL, while enabling their execution with superior performance and scalability.
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。
