TiVEx: Optimized Processing for Time Series Visual Exploration
DocUID: 2023-009 Full Text:
PDF Author: Heba Helal, Mohamed A. Sharaf, Mohammad M. Masud, Panos K. Chrysanthis
Abstract: To facilitate fast-visual data analysis, there is a need for recommending top-k views with "interesting" insights automatically. However, working with high-dimensional time series data makes the process of view recommendations difficult. The primary obstacle lies in finding an automatic way to generate views with less processing time (efficiency) while still closely aligning with the ground truth (effectiveness). In this paper, we propose TiVEx (Time Series Visual Exploration), a technique to address this challenge. TiVEx aims to achieve a balance between efficiency and effectiveness in generating view recommendations. Through extensive experiments, we demonstrate significant cost savings achieved by TiVEx, indicating its efficiency. Furthermore, our analysis delves into the exploration of striking the right balance between efficiency and effectiveness.
Keywords: Visualization, Recommendation, Time series data, Optimization
Published In: 20th ACS/IEEE International Conference on Computer Systems and Applications
Pages: 1-8
Place Published: Giza, Egypt
Year Published: 2023
DOI: 10.1109/AICCSA59173.2023.10479252
Project: TiVEx Subject Area: Data Exploration, Time Series, View Recommendation
Publication Type: Conference Paper
Sponsor: UAE University grant 12R147