Learning for Spatio-temporal and Relational Data

The vast amounts of spatio-temporal data generated by a variety of devices are best utilized in conjunction with relational, tabular data rather than being referenced separately. To enhance the efficiency of data analysis, learned models are frequently used to approximate query results by increasing responsiveness at the cost of some accuracy. Machine learning techniques exist for spatio-temporal data and tabular data separately, but it is not straightforward to represent the combined data in a unified learned model. This paper explores the challenge of learning from heterogeneous data, particularly trajectory and tabular data, and proposes approaches for representation learning using probabilistic circuits and deep neural networks.