Conjugate operators for transparent, explorable research outputs
CoRR(2024)
摘要
Charts, figures, and text derived from data play an important role in
decision making, from data-driven policy development to day-to-day choices
informed by online articles. Making sense of, or fact-checking, outputs means
understanding how they relate to the underlying data. Even for domain experts
with access to the source code and data sets, this poses a significant
challenge. In this paper we introduce a new program analysis framework which
supports interactive exploration of fine-grained I/O relationships directly
through computed outputs, making use of dynamic dependence graphs. Our main
contribution is a novel notion in data provenance which we call related inputs,
a relation of mutual relevance or "cognacy" which arises between inputs when
they contribute to common features of the output. Queries of this form allow
readers to ask questions like "What outputs use this data element, and what
other data elements are used along with it?". We show how Jonsson and Tarski's
concept of conjugate operators on Boolean algebras appropriately characterises
the notion of cognacy in a dependence graph, and give a procedure for computing
related inputs over such a graph.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要