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1. Approximate summaries for why and why-not provenance

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   Lee, Seokki; Ludäscher, Bertram; Glavic, Boris (February 2020, Proceedings of the VLDB Endowment)

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3. Why Study Geysers?

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   Hurwitz, Shaul; Manga, Michael; Campbell, Kathleen; Mu�oz-Saez, Carolina; Eibl, Eva (July 2021, Eos)

   Aside from captivating our senses, geysers have much to tell us about subsurface fluids, climate change effects, and the occurrence and limits of life on Earth and elsewhere in the solar system. 

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4. Why Flash Type Matters: A Statistical Analysis: Why Flash Type Matters

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   Mecikalski, Retha M.; Bitzer, Phillip M.; Carey, Lawrence D. (September 2017, Geophysical Research Letters)

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5. PUG: a framework and practical implementation for why and why-not provenance

   https://doi.org/10.1007/s00778-018-0518-5  

   Lee, Seokki; Ludäscher, Bertram; Glavic, Boris (July 2018, The VLDB Journal)

   Explaining why an answer is (or is not) returned by a query is important for many applications including auditing, debugging data and queries, and answering hypothetical questions about data. In this work, we present the first practical approach for answering such questions for queries with negation (first-order queries). Specifically, we introduce a graph-based provenance model that, while syntactic in nature, supports reverse reasoning and is proven to encode a wide range of provenance models from the literature. The implementation of this model in our PUG (Provenance Unification through Graphs) system takes a provenance question and Datalog query as an input and generates a Datalog program that computes an explanation, i.e., the part of the provenance that is relevant to answer the question. Furthermore, we demonstrate how a desirable factorization of provenance can be achieved by rewriting an input query. We experimentally evaluate our approach demonstrating its efficiency. 

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