Correlation Sketches for Approximate Join-Correlation Queries
Correlation Sketches for Approximate Join-Correlation Queries
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近似连接相关查询的相关草图
DOI：
10.1145/3448016.3458456
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
J. Freire
中科院分区：
文献类型：
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作者：
Aécio S. R. Santos;Aline Bessa;F. Chirigati;Christopher Musco;J. Freire
从Web表和开放数据门户到企业数据，结构化数据集的可用性越来越高，这为通过关系数据增强来丰富分析和改进机器学习模型提供了机会。在本文中，我们引入了一类新的数据增强查询：连接相关查询。给定查询表TQ中的列Q和连接列KQ，在数据集集合中检索表TX，使TX与KQ上的TQ可接合，并且有列C∈TX，使Q与C相关。评估这些查询的naïve方法首先找到可接合的表，然后显式地连接并计算Q与所发现表的所有列之间的相关性，这是非常昂贵的。为了有效地支持相关列发现，我们1)提出了一种草图方法，该方法可以为大量表构建索引，并为连接相关查询提供准确的估计，2)探索了不同的评分策略，根据列与查询的关联程度有效地对查询结果进行排序。我们使用合成数据和真实数据进行了详细的实验评估，结果表明我们的草图具有较高的准确性，评分策略导致了高质量的排名。
The increasing availability of structured datasets, from Web tables and open-data portals to enterprise data, opens up opportunities to enrich analytics and improve machine learning models through relational data augmentation. In this paper, we introduce a new class of data augmentation queries: join-correlation queries. Given a column Q and a join column KQ from a query table TQ, retrieve tables TX in a dataset collection such that TX is joinable with TQ on KQ and there is a column C ∈ TX such that Q is correlated with C. A naïve approach to evaluate these queries, which first finds joinable tables and then explicitly joins and computes correlations between Q and all columns of the discovered tables, is prohibitively expensive. To efficiently support correlated column discovery, we 1) propose a sketching method that enables the construction of an index for a large number of tables and that provides accurate estimates for join-correlation queries, and 2) explore different scoring strategies that effectively rank the query results based on how well the columns are correlated with the query. We carry out a detailed experimental evaluation, using both synthetic and real data, which shows that our sketches attain high accuracy and the scoring strategies lead to high-quality rankings.
DOI：
10.1145/3318464.3389726
发表时间：
2020-06
期刊：
Proceedings. ACM-SIGMOD International Conference on Management of Data
影响因子：
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作者：
Zhang Y;Ives ZG
通讯作者：
Ives ZG