Worst-case Analysis for Interactive Evaluation of Boolean Provenance
Antoine Amarilli, Yael Amsterdamer

TL;DR
This paper provides a worst-case analysis framework for interactive Boolean provenance evaluation, introducing evasive expressions, and identifying classes where optimal probing strategies are computationally feasible or hard.
Contribution
It introduces a worst-case analysis approach inspired by decision tree depth, characterizes evasive expressions, and identifies tractable subclasses for optimal probing strategies.
Findings
Read-once expressions are evasive.
Acyclic monotone 2-DNF expressions have decidable evasiveness.
Optimal strategies are coNP-hard in general, but tractable for certain subclasses.
Abstract
In recent work, we have introduced a framework for fine-grained consent management in databases, which combines Boolean data provenance with the field of interactive Boolean evaluation. In turn, interactive Boolean evaluation aims at unveiling the underlying truth value of a Boolean expression by frugally probing the truth values of individual values. The required number of probes depends on the Boolean provenance structure and on the (a-priori unknown) probe answers. Prior work has analyzed and aimed to optimize the expected number of probes, where expectancy is with respect to a probability distribution over probe answers. This paper gives a novel worst-case analysis for the problem, inspired by the decision tree depth of Boolean functions. Specifically, we introduce a notion of evasive provenance expressions, namely expressions, where one may need to probe all variables in the…
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Taxonomy
TopicsScientific Computing and Data Management · Software Engineering Research · Explainable Artificial Intelligence (XAI)
