Approximate Summaries for Why and Why-not Provenance (Extended Version)

TL;DR

This paper introduces a novel approximate summarization method for large-scale why and why-not provenance data, using pattern-based encoding and sampling techniques to improve scalability, informativeness, and usability.

Contribution

It presents the first scalable approach to generate concise, comprehensive, and meaningful summaries of large provenance datasets using pattern encoding and sampling.

Findings

Scales to large datasets effectively.

Produces concise and informative provenance summaries.

Balances informativeness, conciseness, and completeness.

Abstract

Why and why-not provenance have been studied extensively in recent years. However, why-not provenance, and to a lesser degree why provenance, can be very large resulting in severe scalability and usability challenges. In this paper, we introduce a novel approximate summarization technique for provenance which overcomes these challenges. Our approach uses patterns to encode (why-not) provenance concisely. We develop techniques for efficiently computing provenance summaries balancing informativeness, conciseness, and completeness. To achieve scalability, we integrate sampling techniques into provenance capture and summarization. Our approach is the first to scale to large datasets and to generate comprehensive and meaningful summaries.