Robot-assisted thoracoscopic lymphadenectomy along bilateral RLNs was technically feasible and safe. Skeletonization of the RLNs yields more lymph nodes, but efforts should be made to decrease the incidence of RLN palsy.

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Explaining why an answer is (not) in the result of a query has proven to be of immense importance for many applications. However, why-not provenance, and to a lesser degree also why-provenance, can be very large, even for small input datasets. The resulting scalability and usability issues have limited the applicability of provenance. We present PUG, a system for why and why-not provenance that applies a range of novel techniques to overcome these challenges. Specifically, PUG limits provenance capture to what is relevant to explain a (missing) result of interest and uses an efficient sampling-based summarization method to produce compact explanations for (missing) answers. Using two real-world datasets, we demonstrate how a user can draw meaningful insights from explanations produced by PUG.

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. PVLDB Reference Format:Seokki Lee, Bertram Ludäscher, Boris Glavic. Approximate Summaries for Why and Why-not Provenance. PVLDB, 13(6): xxxxyyyy, 2020.