使用配置文件的可扩展数据发现
Scalable Data Discovery Using Profiles
摘要：
我们研究了大规模发现可连接数据集的问题。也就是说，如何在大量独立的、异构的数据集中自动发现可以连接的属性对。精确（例如，基于不同的值）和基于散列的（例如，基于局部敏感散列）技术需要索引整个数据集，这在规模上是无法实现的。为了克服这个问题，我们从学习的角度来处理这个问题，依赖于配置文件。这些是捕获数据集的模式和数据值的基本特征的简洁表示，可以以分布式和并行的方式有效地提取。然后比较配置文件，以预测来自不同数据集的一对属性之间的连接操作的质量。与现有技术相比，我们定义了一个新的连接质量概念，它依赖于一个度量，同时考虑候选属性之间的包容性和基数比例。我们在一个名为NextiaJD的系统中实现了我们的方法，并进行了大量的实验，以显示我们的方法的预测性能和计算效率。我们的实验表明，NextiaJD获得了与基于哈希的方法相似的预测性能，但我们能够扩展到更大的数据量。此外，NextiaJD产生的误报量要少得多，这是一个理想的大规模功能。
英文摘要：
We study the problem of discovering joinable datasets at scale. This is, how to automatically discover pairs of attributes in a massive collection of independent, heterogeneous datasets that can be joined. Exact (e.g., based on distinct values) and hash-based (e.g., based on locality-sensitive hashing) techniques require indexing the entire dataset, which is unattainable at scale. To overcome this issue, we approach the problem from a learning perspective relying on profiles. These are succinct representations that capture the underlying characteristics of the schemata and data values of datasets, which can be efficiently extracted in a distributed and parallel fashion. Profiles are then compared, to predict the quality of a join operation among a pair of attributes from different datasets. In contrast to the state-of-the-art, we define a novel notion of join quality that relies on a metric considering both the containment and cardinality proportions between candidate attributes. We implement our approach in a system called NextiaJD, and present extensive experiments to show the predictive performance and computational efficiency of our method. Our experiments show that NextiaJD obtains similar predictive performance to that of hash-based methods, yet we are able to scale-up to larger volumes of data. Also, NextiaJD generates a considerably less amount of false positives, which is a desirable feature at scale.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
