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

Distributed Ledger for Provenance Tracking of Artificial Intelligence Assets

High availability of data is responsible for the current trends in Artificial Intelligence (AI) and Machine Learning (ML). However, high-grade datasets are reluctantly shared between actors because of lacking trust and fear of losing control. Provenance tracing systems are a possible measure to build trust by improving transparency. Especially the tracing of AI assets along complete AI value chains bears various challenges such as trust, privacy, confidentiality, traceability, and fair remuneration. In this paper we design a graph-based provenance model for AI assets and their relations within an AI value chain. Moreover, we propose a protocol to exchange AI assets securely to selected parties. The provenance model and exchange protocol are then combined and implemented as a smart contract on a permission-less blockchain. We show how the smart contract enables the tracing of AI assets in an existing industry use case while solving all challenges. Consequently, our smart contract helps to increase traceability and transparency, encourages trust between actors and thus fosters collaboration between them

Language-integrated provenance by trace analysis

Language-integrated provenance builds on language-integrated query techniques to make provenance information explaining query results readily available to programmers. In previous work we have explored language-integrated approaches to provenance in Links and Haskell. However, implementing a new form of provenance in a language-integrated way is still a major challenge. We propose a self-tracing transformation and trace analysis features that, together with existing techniques for type-directed generic programming, make it possible to define different forms of provenance as user code. We present our design as an extension to a core language for Links called LinksT, give examples showing its capabilities, and outline its metatheory and key correctness properties.Comment: DBPL 201

Tracing where and who provenance in linked data - a calculus -

Linked Data provides some sensible guidelines for publishing and consuming data on the Web. Data published on the Web has no inherent truth, yet its quality can often be assessed based on its provenance. This work introduces a new approach to provenance for Linked Data. The simplest notion of provenance-viz., a named graph indicating where the data is now-is extended with a richer provenance format. The format reflects the behaviour of processes interacting with Linked Data, tracing where the data has been published and who published it. An executable model is presented based on abstract syntax and operational semantics, providing a proof of concept and the means to statically evaluate provenance driven access control using a type system