COBRA：基于来源抽象的假设推理压缩
COBRA: Compression via Abstraction of Provenance for Hypothetical Reasoning
摘要：
数据分析通常涉及假设推理：重复修改数据并观察对以数据为中心的应用程序的计算结果的影响。最近的工作提出了利用数据出处跟踪的想法来支持有效的假设推理：而不是昂贵的底层应用程序的重新执行，可以为预先计算的出处表达式赋值。将这种方法用于大规模数据和复杂应用程序的主要挑战在于来源的大小。为此，我们提出了一个框架，允许减少出处的大小。我们的方法是基于使用抽象降低出处粒度。我们提出了一个演示的COBRA，一个系统，允许检查的出处压缩预期的分析结果的影响。我们将展示COBRA在业务数据分析中的有用性。
英文摘要：
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.
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