Fantastic Data and How to Query Them

TL;DR

This paper proposes a unified framework for managing and querying diverse datasets in AI, aiming to streamline data access and enhance understanding across different AI subfields.

Contribution

It introduces a vision for a unified dataset management framework and demonstrates its application in computer vision datasets.

Findings

Framework enables easy integration and querying of datasets.

Improves efficiency in training and deploying AI models.

Facilitates better understanding of data for data-centric AI.

Abstract

It is commonly acknowledged that the availability of the huge amount of (training) data is one of the most important factors for many recent advances in Artificial Intelligence (AI). However, datasets are often designed for specific tasks in narrow AI sub areas and there is no unified way to manage and access them. This not only creates unnecessary overheads when training or deploying Machine Learning models but also limits the understanding of the data, which is very important for data-centric AI. In this paper, we present our vision about a unified framework for different datasets so that they can be integrated and queried easily, e.g., using standard query languages. We demonstrate this in our ongoing work to create a framework for datasets in Computer Vision and show its advantages in different scenarios. Our demonstration is available at https://vision.semkg.org.