A1 Journal article (refereed)
TBSSvis : Visual analytics for Temporal Blind Source Separation (2022)

Piccolotto, N., Bögl, M., Gschwandtner, T., Muehlmann, C., Nordhausen, K., Filzmoser, P., & Miksch, S. (2022). TBSSvis : Visual analytics for Temporal Blind Source Separation. Visual Informatics, 6(4), 51-66. https://doi.org/10.1016/j.visinf.2022.10.002

JYU authors or editors

Publication details

All authors or editors: Piccolotto, Nikolaus; Bögl, Markus; Gschwandtner, Theresia; Muehlmann, Christoph; Nordhausen, Klaus; Filzmoser, Peter; Miksch, Silvia

Journal or series: Visual Informatics

ISSN: 2468-502X

eISSN: 2468-502X

Publication year: 2022

Publication date: 25/10/2022

Volume: 6

Issue number: 4

Pages range: 51-66

Publisher: Zhejiang University Press; Elsevier

Publication country: China

Publication language: English

DOI: https://doi.org/10.1016/j.visinf.2022.10.002

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/84202

Web address of parallel published publication (pre-print): https://arxiv.org/abs/2011.09896

Abstract

Temporal Blind Source Separation (TBSS) is used to obtain the true underlying processes from noisy temporal multivariate data, such as electrocardiograms. TBSS has similarities to Principal Component Analysis (PCA) as it separates the input data into univariate components and is applicable to suitable datasets from various domains, such as medicine, finance, or civil engineering. Despite TBSS’s broad applicability, the involved tasks are not well supported in current tools, which offer only text-based interactions and single static images. Analysts are limited in analyzing and comparing obtained results, which consist of diverse data such as matrices and sets of time series. Additionally, parameter settings have a big impact on separation performance, but as a consequence of improper tooling, analysts currently do not consider the whole parameter space. We propose to solve these problems by applying visual analytics (VA) principles. Our primary contribution is a design study for TBSS, which so far has not been explored by the visualization community. We developed a task abstraction and visualization design in a user-centered design process. Task-specific assembling of well-established visualization techniques and algorithms to gain insights in the TBSS processes is our secondary contribution. We present TBSSvis, an interactive web-based VA prototype, which we evaluated extensively in two interviews with five TBSS experts. Feedback and observations from these interviews show that TBSSvis supports the actual workflow and combination of interactive visualizations that facilitate the tasks involved in analyzing TBSS results.

Keywords: time series; time-series analysis; signal processing

Free keywords: blind source separation; ensemble visualization; visual analytics; parameter space exploration

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2022

JUFO rating: 1