Tiramisù: Making Sense of Multi-Faceted Process Information Through Time and Space

Author:

Alman Anti1,Arleo Alessio2,Beerepoot Iris3,Burattin Andrea4,Ciccio Claudio Di3,Resinas Manuel5

Affiliation:

1. University of Tartu

2. Eindhoven University of Technology

3. Utrecht University

4. Technical University of Denmark

5. University of Seville

Abstract

Abstract

Knowledge-intensive processes represent a particularly challenging scenario for process mining. The flexibility that such processes allow constitutes a hurdle as they are hard to capture in a single model. To tackle this problem, multiple visual representations of the same processes could be beneficial, each addressing different information dimensions according to the specific needs and background knowledge of the concrete process workers and stakeholders. In this paper, we propose, describe, and evaluate a framework, named Tiramisù, that leverages visual analytics for the interactive visualization of multi-faceted process information, aimed at supporting the investigation and insight generation of users in their process analysis tasks. Tiramisù is based on a multi-layer visualization methodology that includes a visual backdrop that provides context and an arbitrary number of superimposed and on-demand dimension layers. This arrangement allows our framework to display process information from different perspectives and to project this information onto a domain-friendly representation of the context in which the process unfolds. We provide an in-depth description of the approach's founding principles, deeply rooted in visualization research, that justify our design choices for the whole framework. We demonstrate the feasibility of the framework through its application in two use-case scenarios in the context of healthcare and personal information management. Plus, we conducted qualitative evaluations with potential end users of both scenarios, gathering precious insights about the efficacy and applicability of our framework to various application domains.

Publisher

Springer Science and Business Media LLC

Reference52 articles.

1. Iris Beerepoot and others (2023) The biggest business process management problems to solve before we die. Comput. Ind. 146: 103837 https://doi.org/10.1016/j.compind.2022.103837, dblp computer science bibliography, https://dblp.org, https://dblp.org/rec/journals/cii/BeerepootCRRBBCCCDFDDFFGIIKKLLLLMMM23.bib, Mon, 26 Jun 2023 20:57:33 +0200, https://doi.org/10.1016/j.compind.2022.103837

2. Tamara Munzner (2009) A Nested Process Model for Visualization Design and Validation. {IEEE} Trans. Vis. Comput. Graph. 15(6): 921--928 https://doi.org/10.1109/TVCG.2009.111, dblp computer science bibliography, https://dblp.org, https://dblp.org/rec/journals/tvcg/Munzner09.bib, Wed, 14 Nov 2018 10:22:03 +0100, https://doi.org/10.1109/TVCG.2009.111

3. Shneiderman, Ben (1996) The eyes have it: A task by data type taxonomy for information visualizations. 10.1109/VL.1996.545307, IEEE, 336--343, VL, Proceedings 1996 IEEE symposium on visual languages

4. Raidou, Renata Georgia (2019) Visual analytics for the representation, exploration, and analysis of high-dimensional, multi-faceted medical data. Biomedical Visualisation: Volume 2 : 137--162 https://doi.org/10.1007/978-3-030-14227-8_10, Springer

5. Jesus J. Caban and David Gotz (2015) Visual analytics in healthcare - opportunities and research challenges. J. Am. Medical Informatics Assoc. 22(2): 260--262 https://doi.org/10.1093/jamia/ocv006, dblp computer science bibliography, https://dblp.org, https://dblp.org/rec/journals/jamia/CabanG15.bib, Mon, 11 May 2020 23:00:34 +0200, https://doi.org/10.1093/jamia/ocv006

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