Towards a Unified Graph Model for Supporting Data Management and Usable Machine Learning

Data management and machine learning are two important tasks in data science. However, they have been independently studied so far. We argue that they should be complementary to each other. On the one hand, machine learning requires data management techniques to extract, integrate, clean the data, to support scalable and usable machine learning, making it user-friendly and easily deployable. On the other hand, data management relies on machine learning techniques to curate data and improve its quality. This requires database systems to treat machine learning algorithms as their basic operators, or at the very least, optimizable stored procedures. It poses new challenges as machine learning tasks tend be iterative and recursive in nature, and some models have to be tweaked and retrained. This calls for a reexamination of database design to make it machine learning friendly. In this position paper, we present a preliminary design of a graph model for supporting both data management and usable machine learning. To make machine learning usable, we provide a declarative query language, that extends SQL to support data management and machine learning operators, and provide visualization tools. To optimize data management procedures, we devise graph optimization techniques to support a finer-grained optimization than traditional tree-based optimization model. We also present a workflow to support machine learning (ML) as a service to facilitate model reuse and implementation, making it more usable and discuss emerging research challenges in unifying data management and machine learning.