A Survey on Data Collection for Machine Learning: A Big Data-AI Integration Perspective
A Survey on Data Collection for Machine Learning: A Big Data-AI Integration Perspective
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DOI：
10.1109/tkde.2019.2946162
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发表时间：
2021-04-01
影响因子：
8.9
通讯作者：
Whang, Steven Euijong
中科院分区：
文献类型：
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作者：
Roh, Yuji;Heo, Geon;Whang, Steven Euijong
数据收集是机器学习中的主要瓶颈，也是多个社区的活跃研究主题。数据收集最近已成为一个关键问题，这主要有两个原因。首先，随着机器学习变得越来越广泛，我们看到的新应用程序不一定具有足够的标记数据。其次，与传统的机器学习不同，深度学习技术会自动生成功能，从而节省了功能工程成本，但是作为回报，可能需要大量的标记数据。有趣的是，数据收集的最新研究不仅来自机器学习，自然语言和计算机视觉社区，而且还来自数据管理社区，因为处理大量数据的重要性。在这项调查中，我们从数据管理的角度对数据收集进行了全面研究。数据收集在很大程度上包括数据获取，数据标签以及现有数据或模型的改进。我们提供这些操作的研究格局，提供有关在何时使用哪种技术的准则，并确定有趣的研究挑战。机器学习和数据管理的数据收集是大数据和人工智能（AI）集成的更大趋势的一部分，并为新研究打开了许多机会。
Data collection is a major bottleneck in machine learning and an active research topic in multiple communities. There are largely two reasons data collection has recently become a critical issue. First, as machine learning is becoming more widely-used, we are seeing new applications that do not necessarily have enough labeled data. Second, unlike traditional machine learning, deep learning techniques automatically generate features, which saves feature engineering costs, but in return may require larger amounts of labeled data. Interestingly, recent research in data collection comes not only from the machine learning, natural language, and computer vision communities, but also from the data management community due to the importance of handling large amounts of data. In this survey, we perform a comprehensive study of data collection from a data management point of view. Data collection largely consists of data acquisition, data labeling, and improvement of existing data or models. We provide a research landscape of these operations, provide guidelines on which technique to use when, and identify interesting research challenges. The integration of machine learning and data management for data collection is part of a larger trend of Big data and Artificial Intelligence (AI) integration and opens many opportunities for new research.