论文标题
COBRA：通过抽象的来源进行假设推理的压缩
COBRA: Compression via Abstraction of Provenance for Hypothetical Reasoning
论文作者
论文摘要
数据分析通常涉及假设推理：反复修改数据并观察以数据为中心应用程序的计算结果的诱导影响。最近的工作提议利用数据出处跟踪的想法来支持有效的假设推理：而不是对基础应用程序的昂贵重新执行，而是可以将值分配给预计的出处表达式。利用这种方法进行大规模数据和复杂应用程序的主要挑战在于出处的大小。为此，我们提出了一个允许减少出处大小的框架。我们的方法基于使用抽象降低出处粒度。我们提出了COBRA的演示，该系统允许检查出处压缩对预期分析结果的影响。我们将在业务数据分析的背景下证明眼镜蛇的有用性。
Data analytics often involves hypothetical reasoning: repeatedly modifying the data and observing the induced effect on the computation result of a data-centric application. Recent work has proposed to leverage ideas from data provenance tracking towards supporting efficient hypothetical reasoning: instead of a costly re-execution of the underlying application, one may assign values to a pre-computed provenance expression. A prime challenge in leveraging this approach for large-scale data and complex applications lies in the size of the provenance. To this end, we present a framework that allows to reduce provenance size. Our approach is based on reducing the provenance granularity using abstraction. We propose a demonstration of COBRA, a system that allows examine the effect of the provenance compression on the anticipated analysis results. We will demonstrate the usefulness of COBRA in the context of business data analysis.
