一个神经数据库,用于回答关于不完整的关系数据的聚合查询
Sepanta Zeighami1, Raghav Seshadri1, Cyrus Shahabi1
1University of Southern California, CA 90089.
概括
在不完整的数据集上,NeuroComplete直接估计了对聚合查询的答案,避免了复杂的合成数据生成. 与现有方法相比,这种新的方法显著减少了平均和计数查询的错误.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 由于成本,隐私或集成问题,现实世界的数据集经常不完整.
- 不完整的数据导致对汇总查询的答案不准确.
- 现有的解决方案涉及合成数据生成,这具有挑战性,容易产生偏见.
研究的目的:
- 通过直接估计查询答案,提出处理不完整数据集的范式转变.
- 引入NeuroComplete,一种绕过合成数据生成的方法.
- 为了提高对不完整的关系数据的聚合查询答案的准确性.
主要方法:
- 从不完整的数据集中,NeuroComplete生成了带有可计算答案的查询.
- 查询被嵌入到一个特征空间中,表示它们与相关数据部分的关系.
- 一个神经网络是使用监督学习与查询功能和正确答案训练的神经网络.
主要成果:
- 神经完整模型学会了对新查询的答案进行概括和准确估计.
- 实验结果显示,对现实数据集的错误有显著的减少.
- 与最先进的方法相比,在AVG查询中减少了4倍的错误,在COUNT查询中减少了10倍.
结论:
- 对于不完整的数据集,NeuroComplete为合成数据生成提供了一个有效的替代方案.
- 这种方法利用神经网络和查询嵌入来准确地回答聚合查询.
- 该方法在减少常见聚合查询类型的错误方面表现出卓越的性能.
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