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Award ID contains: 1910108

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  1. ; (, Proceedings of the VLDB Endowment)
    null (Ed.)
    Data provenance tools aim to facilitate reproducible data science and auditable data analyses, by tracking the processes and inputs responsible for each result of an analysis. Fine-grained provenance further enables sophisticated reasoning about why individual output results appear or fail to appear. However, for reproducibility and auditing, we need a provenance archival system that is tamper-resistant , and efficiently stores provenance for computations computed over time (i.e., it compresses repeated results). We study this problem, developing solutions for storing fine-grained provenance in relational storage systems while both compressing and protecting it via cryptographic hashes. We experimentally validate our proposed solutions using both scientific and OLAP workloads. 
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  2. ; (, Proceedings of the 2020 International Conference on Management of Data, SIGMOD Conference 2020)
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