  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.
