In this work, we track the lineage of tuples throughout their database lifetime. That is, we consider a scenario in which tuples (records) that are produced by a query may affect other tuple insertions into the DB, as part of a normal workflow. As time goes on, exact provenance explanations for such tuples become deeply nested, increasingly consuming space, and resulting in decreased clarity and readability. We present a novel approach for approximating lineage tracking, using a Machine Learning (ML) and Natural Language Processing (NLP) technique; namely, word embedding. The basic idea is summarizing (and approximating) the lineage of each tuple via a small set of constant-size vectors (the number of vectors per-tuple is a hyperparameter). Therefore, our solution does not suffer from space complexity blow-up over time, and it "naturally ranks" explanations to the existence of a tuple. We devise an alternative and improved lineage tracking mechanism, that of keeping track of and querying lineage at the column level; thereby, we manage to better distinguish between the provenance features and the textual characteristics of a tuple. We integrate our lineage computations into the PostgreSQL system via an extension (ProvSQL) and experimentally exhibit useful results in terms of accuracy against exact, semiring-based, justifications. In the experiments, we focus on tuples with multiple generations of tuples in their lifelong lineage and analyze them in terms of direct and distant lineage. The experiments suggest a high usefulness potential for the proposed approximate lineage methods and the further suggested enhancements. This especially holds for the column-based vectors method which exhibits high precision and high per-level recall.

中文翻译：

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数据库中基于机器学习的沿袭

在这项工作中，我们在整个数据库生命周期中跟踪元组的沿袭。也就是说，我们考虑这样一种场景，其中查询生成的元组（记录）可能会影响其他元组插入到数据库中，作为正常工作流程的一部分。随着时间的推移，对此类元组的确切出处解释变得深入嵌套，占用空间越来越大，并导致清晰度和可读性降低。我们提出了一种使用机器学习 (ML) 和自然语言处理 (NLP) 技术来近似谱系跟踪的新方法；即词嵌入。基本思想是通过一小组固定大小的向量（每个元组的向量数量是一个超参数）总结（和近似）每个元组的谱系。因此，我们的解决方案不会随着时间的推移而遭受空间复杂性爆炸的影响，并且“