Towards a Rule-based Visualization Recommendation System

Arnab Chakrabarti, Farhad Ahmad, Christoph Quix

2021

Abstract

Data visualization plays an important role in the analysis of data and the identification of insights and characteristics within the dataset. However, visualizing datasets, especially high dimensional ones, is a very difficult and time-consuming process that requires a great deal of manual effort. The automation of data visualization is done in the form of Visualization Recommendation Systems by detecting factors such as data characteristics and user intended tasks in order to recommend useful visualizations. In this paper, we propose a Visualization Recommendation System, built on a knowledge-based rule engine, that takes minimal user input, extracts important data characteristics and supports a large number of visualization techniques depending on both the data characteristics and the intended tasks of the user. Through our proposed model we show the efficacy of such recommendations for users without any domain expertise. Lastly, we evaluate our system with real-world use case scenarios to prove the effectiveness and the feasibility of our approach.

Paper Citation

in Harvard Style

Chakrabarti A., Ahmad F. and Quix C. (2021). Towards a Rule-based Visualization Recommendation System. In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 1: KDIR; ISBN 978-989-758-533-3, SciTePress, pages 57-68. DOI: 10.5220/0010677100003064

in Bibtex Style

@conference{kdir21,
author={Arnab Chakrabarti and Farhad Ahmad and Christoph Quix},
title={Towards a Rule-based Visualization Recommendation System},
booktitle={Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 1: KDIR},
year={2021},
pages={57-68},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010677100003064},
isbn={978-989-758-533-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 1: KDIR
TI - Towards a Rule-based Visualization Recommendation System
SN - 978-989-758-533-3
AU - Chakrabarti A.
AU - Ahmad F.
AU - Quix C.
PY - 2021
SP - 57
EP - 68
DO - 10.5220/0010677100003064
PB - SciTePress