BigSR：现代大数据平台上实时表达RDF流推理的实证研究
BigSR: an empirical study of real-time expressive RDF stream reasoning on modern Big Data platforms
摘要：
语言表达性和系统可伸缩性（E & S）之间的权衡是RDF流推理中的一个众所周知的问题。更高的表达能力支持更复杂的推理逻辑，然而，它也可能阻碍系统的可扩展性。目前的研究主要集中在适合流推理的逻辑框架以及原型系统的实现和评估上。这些系统通常是在一个集中的设置，其固有的可扩展性有限的痛苦，而应用分布式解决方案，以涵盖E & S的深入研究仍然缺失。在本文中，我们的目的是探讨应用现代分布式计算框架，以满足E & S所有一起的可行性。要做到这一点，我们首先提出BigSR，一个技术演示，支持LARS框架的积极片段。为了通用性和覆盖各种各样的用例，BigSR依赖于主要分布式执行框架采用的两种主要执行模型：批量同步处理（BSP）和一次记录（RAT）。因此，我们在Apache Spark Streaming（BSP模型）和Apache Flink（RAT模型）上实现了BigSR。为了总结BSP和RAT对E & S的影响，我们分析了这两种模型支持分布式流推理的能力，并确定了几种类型的用例，其特征在于它们的支持水平。这种分类允许通过评估每种类型的用例\wrt的可伸缩性及其表达水平来量化E & S权衡。然后，我们进行了一系列的实验，从4个不同的数据集的15个查询。我们的实验表明，BSP和RAT上的BigSR通常可以扩展到每秒超过百万三倍的高吞吐量（有或没有递归），RAT达到亚毫秒级的无状态查询操作延迟。
英文摘要：
The trade-off between language expressiveness and system scalability (E&S) is a well-known problem in RDF stream reasoning. Higher expressiveness supports more complex reasoning logic, however, it may also hinder system scalability. Current research mainly focuses on logical frameworks suitable for stream reasoning as well as the implementation and the evaluation of prototype systems. These systems are normally developed in a centralized setting which suffer from inherent limited scalability, while an in-depth study of applying distributed solutions to cover E&S is still missing. In this paper, we aim to explore the feasibility of applying modern distributed computing frameworks to meet E&S all together. To do so, we first propose BigSR, a technical demonstrator that supports a positive fragment of the LARS framework. For the sake of generality and to cover a wide variety of use cases, BigSR relies on the two main execution models adopted by major distributed execution frameworks: Bulk Synchronous Processing (BSP) and Record-at-A-Time (RAT). Accordingly, we implement BigSR on top of Apache Spark Streaming (BSP model) and Apache Flink (RAT model). In order to conclude on the impacts of BSP and RAT on E&S, we analyze the ability of the two models to support distributed stream reasoning and identify several types of use cases characterized by their levels of support. This classification allows for quantifying the E&S trade-off by assessing the scalability of each type of use case \wrt its level of expressiveness. Then, we conduct a series of experiments with 15 queries from 4 different datasets. Our experiments show that BigSR over both BSP and RAT generally scales up to high throughput beyond million-triples per second (with or without recursion), and RAT attains sub-millisecond delay for stateless query operators.
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