Cognacy Queries over Dependence Graphs for Transparent Visualisations

Joseph Bond, Cristina David, Minh Nguyen, Dominic Orchard, Roly Perera

Abstract: Charts, figures, and text derived from data play an important role in decision making. But making sense of or fact-checking outputs means understanding how they relate to the underlying data. Even for experts with access to the source code and data sets, this poses a significant challenge. We introduce a new program analysis framework (A supporting artifact is available at https://zenodo.org/records/14637654 [5].) which supports interactive exploration of fine-grained IO relationships directly through computed outputs, using dynamic dependence graphs. This framework enables a novel notion in data provenance which we call linked inputs, a relation of mutual relevance or cognacy which arises between inputs that contribute to common features of the output. We give a procedure for computing linked inputs over a dependence graph, and show how the presented in this paper is faster on most examples than an implementation based on execution traces.

External IDs:dblp:conf/esop/BondDNOP25