Conjugate operators for transparent, explorable research outputs
<|reference_start|>Conjugate operators for transparent, explorable research outputs: 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.<|reference_end|>
@article{bond2024cognacy,
    title={Cognacy Queries over Dependence Graphs for Transparent Visualisations},
    author={Joseph Bond, Cristina David, Minh Nguyen, Dominic Orchard and Roly
  Perera},
    journal={arXiv preprint arXiv:2403.04403},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.04403},
    primaryClass={cs.PL}
}