Provenance in Databases: Why, How, and Where

By J. Cheney, Laura Chiticariu, Wang Chiew Tan. 2009

775 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Information Systems and Management, Evaluation & Benchmarks

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Citations in the corpus

Datasheets for datasets (2018, background): “But while provenance has been extensively studied in the database literature (Cheney et al., 2009; Bhardwaj et al., 2014), it has received relatively little attention in machine learning.”
Query-Oriented Data Cleaning with Oracles (2015, methodology): “Hence, we suggest an approach that exploits both the crowd and the underlying database 𝐷 which is likely to work well if the underlying database is mostly correct and complete w.r.t. the query 𝑄 .”
PALM: Machine Learning Explanations For Iterative Debugging (2017, result): “This notion is similar to responsibility in the data lineage and explanation literature [4, 17], in the context of black-box machine learning models as opposed to database queries with well deﬁned semantics.”
ProvDB: A System for Lifecycle Management of Collaborative Analysis Workflows (2016, methodology): “Our work aims to combine the two together with version lineages captured by VCS, and provide uniform platform for collaborative data science workﬂows.”
Collaborative Data Analytics with DataHub (2015, background): “DataHub has three key components, designed to support the above use data collaboration use cases:”
PrIU: A Provenance-Based Approach for Incrementally Updating Regression Models (2020, background): “Note that the problem of incrementally updating the model after removing a subset of the training samples can be seen as a question of data provenance [4, 7, 19].”
A survey on provenance: What for? What form? What from? (2017, neutral): “Due to space constraints, we only briefly summarize essential work on data provenance recently surveyed in [46] to the benefit of a more detailed novel survey on provenance research focusing on explaining why some data are not part of a query result, even though they were expected results.”
Aurum: A Data Discovery System (2018, background): “The provenance graph is sufﬁcient to answer why and how provenance questions, as deﬁned in the literature [8].”
Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing (2012, methodology): “We refer the reader to [5] and [9] for surveys of this work.”
Tachyon: Reliable, Memory Speed Storage for Cluster Computing Frameworks (2014, methodology): “[18] and [21] provide detailed surveys of this work.”
Reusable Templates and Guides For Documenting Datasets and Models for Natural Language Processing and Generation: A Case Study of the HuggingFace and GEM Data and Model Cards (2021, background): “Despite existing literature on database documentation in HCI and related ﬁelds (Cheney et al., 2009; Bhardwaj et al., 2014), documentation in ML has only recently gained traction.”