渐进式分析：探索性数据分析的计算范式
Progressive Analytics: A Computation Paradigm for Exploratory Data Analysis
摘要：
探索数据需要从分析师到系统的快速反馈循环，由于人类认知的限制，延迟低于10秒。当数据变得很大或分析变得很复杂时，顺序计算不再能在几秒钟内完成，数据探索也会受到严重阻碍。本文描述了一种称为Progressive Computation for Data Analysis（数据分析的渐进式计算）或更简洁地称为Progressive Analytics（渐进式分析）的新型计算范例，它通过以渐进方式执行计算，在程序设计语言级别提供了低延迟保证。在语言级别移动这种渐进式计算使探索性数据分析系统的程序员免于从头开始以渐进的方式实现整个分析管道，从而简化了可扩展探索性数据分析系统的实现。本文通过一个名为ProgressiVis的原型实现来描述这个新范例，并通过示例解释它所隐含的需求。
英文摘要：
Exploring data requires a fast feedback loop from the analyst to the system, with a latency below about 10 seconds because of human cognitive limitations. When data becomes large or analysis becomes complex, sequential computations can no longer be completed in a few seconds and data exploration is severely hampered. This article describes a novel computation paradigm called Progressive Computation for Data Analysis or more concisely Progressive Analytics, that brings at the programming language level a low-latency guarantee by performing computations in a progressive fashion. Moving this progressive computation at the language level relieves the programmer of exploratory data analysis systems from implementing the whole analytics pipeline in a progressive way from scratch, streamlining the implementation of scalable exploratory data analysis systems. This article describes the new paradigm through a prototype implementation called ProgressiVis, and explains the requirements it implies through examples.
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。
