🤖 AI Summary
Traditional data provenance struggles to capture the uncertainty arising from semantic ambiguities in multi-outcome systems, such as concurrent transactions or Datalog with negation. This work proposes a “deterministic provenance” framework that quantifies the dependency depth of query results on hierarchical semantic decisions by tracking the semantic commitments required to resolve ambiguities. Leveraging an exchange semiring structure over support sets and a filtration mechanism, the framework extends provenance to multi-outcome settings for the first time, unifying the modeling of provenance behavior across diverse semantic variants—including isolation levels and negation handling. It further enables composable robustness analysis of semantic decision paths and quantitative diagnosis of resolution costs.
📝 Abstract
Many data systems admit multiple admissible outcomes for the same input: concurrent transactions may serialize in one of many orders; a logic program may have multiple stable models. Classical data provenance cannot even pose its question in such settings -- it explains how a result was derived, but only after something has chosen which result to produce. We introduce \emph{determination provenance} to track the commitments that resolve this ambiguity. A tuple's \emph{support} is the set of resolutions under which it holds. Supports form a commutative semiring, and layered commitments induce a \emph{filtration} measuring each tuple's \emph{query-relative depth} -- how many layers of semantic resolution it depends on. Positive relational algebra respects the filtration, enabling compositional robustness analysis and quantitative diagnosis of resolution cost. We instantiate the framework for transactional isolation and for $\mbox{Datalog}^\neg$; in both, classical semantic variants (isolation levels; negation semantics) correspond to different views of a single shared filtration.