缺失数据和版本化数据的上下文
Contextuality from missing and versioned data
摘要：
传统的分类数据分析（例如广义线性模型）处理简单的平面数据集，类似于数据库中的单个表，没有丢失数据或冲突版本的概念。相比之下，现代数据分析必须处理具有许多不一定总是一致的局部表的分布式数据库。这些表格的计算代理在空间上是分离的，具有绑定光速约束和数据到达太快，这些分布式视图无法完全知情和全局一致。上下文性是一种数学性质，描述了量子力学中出现的一种不一致性（例如贝尔定理）。在本文中，我们将展示如何在常见的数据汇集场景中出现上下文，包括丢失数据和版本控制（如在采用快照隔离的低延迟分布式数据库中）。在配套文件中，我们开发了适应这种制度的统计模型。
英文摘要：
Traditionally categorical data analysis (e.g. generalized linear models) works with simple, flat datasets akin to a single table in a database with no notion of missing data or conflicting versions. In contrast, modern data analysis must deal with distributed databases with many partial local tables that need not always agree. The computational agents tabulating these tables are spatially separated, with binding speed-of-light constraints and data arriving too rapidly for these distributed views ever to be fully informed and globally consistent. Contextuality is a mathematical property which describes a kind of inconsistency arising in quantum mechanics (e.g. in Bell's theorem). In this paper we show how contextuality can arise in common data collection scenarios, including missing data and versioning (as in low-latency distributed databases employing snapshot isolation). In the companion paper, we develop statistical models adapted to this regime.
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。
