Database Queries that Explain their Work

Provenance for database queries or scientific workflows is often motivated as providing explanation, increasing understanding of the underlying data sources and processes used to compute the query, and reproducibility, the capability to recompute the results on different inputs, possibly specialized to a part of the output. Many provenance systems claim to provide such capabilities; however, most lack formal definitions or guarantees of these properties, while others provide formal guarantees only for relatively limited classes of changes. Building on recent work on provenance traces and slicing for functional programming languages, we introduce a detailed tracing model of provenance for multiset-valued Nested Relational Calculus, define trace slicing algorithms that extract subtraces needed to explain or recompute specific parts of the output, and define query slicing and differencing techniques that support explanation. We state and prove correctness properties for these techniques and present a proof-of-concept implementation in Haskell.Comment: PPDP 201

Toward a Theory of Self-Explaining Computation

Abstract. Provenance techniques aim to increase the reliability of human judgments about data by making its origin and derivation process explicit. Originally motivated by the needs of scientific databases and scientific computation, provenance has also become a major issue for business and government data on the Web. However, so far provenance has been studied only in relatively restrictive settings: typically, for data stored in databases or scientific workflow systems, and processed by query or workflow languages of limited expressiveness. Long-term provenance solutions require an understanding of provenance in other settings, particularly the general-purpose programming or scripting languages that are used to glue different components such as databases, Web services and workflows together. Moreover, what is required is not only an account of mechanisms for recording provenance, but also a theory of what it means for provenance information to explain or justify a computation. In this paper, we begin to outline a such a theory of self-explaining computation. We introduce a model of provenance for a simple imperative language based on operational derivations and explore its properties.