Distributed Discovery of Functional Dependencies

被引：9

作者：

Saxena, Hemant ^{[1
]}

Golab, Lukasz ^{[1
]}

Ilyas, Ihab F. ^{[1
]}

机构：

[1] Univ Waterloo, Waterloo, ON, Canada

来源：

2019 IEEE 35TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2019) | 2019年

关键词：

data profiling; functional dependencies; distributed algorithms;

D O I：

10.1109/ICDE.2019.00149

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We address the problem of discovering functional dependencies from distributed big data. Existing (non distributed) algorithms such as FastFDs focus on minimizing computation. However, distributed algorithms must also optimize data communication costs, especially in shared-nothing settings. We propose a distributed version of FastFDs that is communication-efficient and we experimentally show significant performance improvements over a straightforward distributed implementation.

引用

页码：1590 / 1593

页数：4

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