ArXiv Preprint
Tabular data comprising rows (samples) with the same set of columns
(attributes, is one of the most widely used data-type among various industries,
including financial services, health care, research, retail, and logistics, to
name a few. Tables are becoming the natural way of storing data among various
industries and academia. The data stored in these tables serve as an essential
source of information for making various decisions. As computational power and
internet connectivity increase, the data stored by these companies grow
exponentially, and not only do the databases become vast and challenging to
maintain and operate, but the quantity of database tasks also increases. Thus a
new line of research work has been started, which applies various learning
techniques to support various database tasks for such large and complex tables.
In this work, we split the quest of learning on tabular data into two phases:
The Classical Learning Phase and The Modern Machine Learning Phase. The
classical learning phase consists of the models such as SVMs, linear and
logistic regression, and tree-based methods. These models are best suited for
small-size tables. However, the number of tasks these models can address is
limited to classification and regression. In contrast, the Modern Machine
Learning Phase contains models that use deep learning for learning latent space
representation of table entities. The objective of this survey is to scrutinize
the varied approaches used by practitioners to learn representation for the
structured data, and to compare their efficacy.
Rajat Singh, Srikanta Bedathur
2023-02-23
