Bringing Provenance to Its Full Potential Using Causal Reasoning

Provenance information is often used to explain query results and outcomes, exploit results of prior reasoning, and establish trust in data. The generality of the notion makes it applicable in a variety of domains, including data warehousing [7], curated databases [4], and various scientific applications. The recent introduction of causal reasoning in a database setting exploits provenance in ways that expand its applicability to more complex problems, and establish new directions, making a step towards achieving provenance’s full potential. In this paper we explore through a variety of examples how causality improves on provenance information, discuss the challenges of building causality able systems, and propose some new directions.

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