The data needed for machine learning (ML) model training, can reside in different separate sites often termed data silos. For data-intensive ML applications, data silos pose a major challenge: the integration and transformation of data demand a lot of manual work and computational resources. With data privacy and security constraints, data often cannot leave the local sites, and a model has to be trained in a decentralized manner. In this work, we present a vision on how to bridge the traditional data integration (DI) techniques with the requirements of modern machine learning. We explore the possibilities of utilizing metadata obtained from data integration processes for improving the effectiveness and efficiency of ML models. We analyze two common use cases over data silos, feature augmentation and federated learning. Bringing data integration and machine learning together, we highlight the new research opportunities from the aspects of systems, representations, factorized learning and federated learning.

翻译：机器学习模型训练所需的数据可能分布在不同的独立站点，即所谓的数据孤岛中。对于数据密集型机器学习应用而言，数据孤岛带来了一项重大挑战：数据的整合与转换需要大量的人工工作和计算资源。在数据隐私和安全约束下，数据往往无法离开本地站点，模型必须以去中心化的方式进行训练。本文提出了一种愿景：如何将传统数据整合（DI）技术与现代机器学习的需求相衔接。我们探讨了利用从数据整合过程中获取的元数据来提升机器学习模型有效性和效率的可能性。我们分析了数据孤岛上的两个常见用例：特征增强和联邦学习。通过将数据整合与机器学习相结合，我们从系统、表示、因子化学习及联邦学习等角度，突显了新的研究机遇。