ProvCite: Provenance-based Data Citation
ProvCite: Provenance-based Data Citation
复制标题
ProvCite：基于来源的数据引用
DOI：
--
复制
发表时间：
2019
影响因子：
2.5
通讯作者：
S. Davidson
中科院分区：
文献类型：
--
作者：
Yinjun Wu;Abdussalam Alawini;Daniel Deutch;Tova Milo;S. Davidson
随着研究产品扩展到包括结构化数据集，如何自动生成对此类数据集的任意查询结果的引用就出现了挑战。先前的工作在以下背景下探讨了这个问题
连词
使用基于重写的模型 (RBM) 的查询和视图。然而，越来越多的科学问题
聚合，
例如基础数据的统计摘要，RBM 无法轻易扩展。在本文中，我们展示了如何利用基于来源的模型（PBM）来：1）生成对连接以及聚合查询和视图的引用； 2) 将引用与各个结果元组关联起来，以便能够引用结果集的任意子集（
细粒度引用
）； 3) 进行优化以返回引用
可以接受的时间。
我们在 ProvCite 中实现的 PBM 表明，它不仅可以处理比 RBM 更大类别的查询和视图，而且在某些情况下，当仅限于联合视图时，其性能也优于 RBM。
As research products expand to include structured datasets, the challenge arises of how to automatically generate citations to the results of arbitrary queries against such datasets. Previous work explored this problem in the context of
conjunctive
queries and views using a Rewriting-Based Model (RBM). However, an increasing number of scientific queries are
aggregate,
e.g. statistical summaries of the underlying data, for which the RBM cannot be easily extended. In this paper, we show how a Provenance-Based Model (PBM) can be leveraged to 1) generate citations to conjunctive as well as aggregate queries and views; 2) associate citations with individual result tuples to enable arbitrary subsets of the result set to be cited (
fine-grained citations
); and 3) be optimized to return citations in
acceptable time.
Our implementation of PBM in ProvCite shows that it not only handles a larger class of queries and views than RBM, but can outperform it when restricted to conjunctive views in some cases.
GProM - 满足您出处需求的瑞士军刀
DOI：
--
发表时间：
2018
期刊：
A Quarterly bulletin of the Computer Society of the IEEE Technical Committee on Data Engineering
影响因子：
--
作者：
Arab, B. S.;Feng, S.;Glavic, B.;Lee, S.;Niu, X.;Zeng, Q.
通讯作者：
Zeng, Q.