Related papers: Fantastic Data and How to Query Them

Fantastic Data and How to Query Them

URL: http://arxiv.org/abs/2201.05026v1
Date: Thu, 13 Jan 2022 15:24:46 GMT
Title: Fantastic Data and How to Query Them
Authors: Trung-Kien Tran, Anh Le-Tuan, Manh Nguyen-Duc, Jicheng Yuan, Danh Le-Phuoc
Abstract summary: We present our vision about a unified framework for different datasets so that they can be integrated and easily queried. We demonstrate this in our ongoing work to create a framework for datasets in Computer Vision and show its advantages in different scenarios.
Score: 3.464871689508835
License: http://creativecommons.org/licenses/by/4.0/
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.

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