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Exploring Dynamic Hypergraphs for Clustering Analysis of District Heating Data
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0002-9527-4594
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0001-6745-4398
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0002-3010-8798
NODA Intelligent Systems AB, Karlshamn, Sweden.ORCID iD: 0009-0008-0923-0933
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2025 (English)In: 18th International Symposium on Visual Information Communication and Interaction, VINCI 2025 / [ed] Wallner G., She J., Burch M., Liang H.-N., Association for Computing Machinery (ACM), 2025, article id 7Conference paper, Published paper (Refereed)
Abstract [en]

In the District Heating (DH) sector, the analysis and monitoring of data from DH substations is crucial to keeping the entire DH network running efficiently. Clustering of DH substations based on multivariate data helps analyze their behavior over time. In this context, a visualization-based analysis approach can be particularly beneficial. In this paper, we explore the use of dynamic hypergraph visualization to analyze the clustering results of DH network substations over time. We present the initial results of designing and implementing a visual analytics dashboard that supports DH experts in analyzing different behaviors of DH substations. In the proposed dashboard, we adopt the Parallel Aggregated Ordered Hypergraph (PAOH) technique to visualize dynamic hypergraphs, which provides a compact visualization of multivariate data clustering over time. Moreover, we include additional views with complementary visualizations supporting the analysis and understanding of the dynamic hypergraph main view. We showcase the capability of our dashboard applied on a real DH dataset.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2025. article id 7
Keywords [en]
Clustering Analysis, District Heating, Dynamic Hypergraph, Temporal Hypergraph, Visual Analytics
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-29084DOI: 10.1145/3769534.3769564ISI: 001667060900007Scopus ID: 2-s2.0-105026250295ISBN: 9798400718458 (print)OAI: oai:DiVA.org:bth-29084DiVA, id: diva2:2026363
Conference
18th International Symposium on Visual Information Communication and Interaction, VINCI 2025, Linz, Dec 1-3, 2025
Part of project
HINTS - Human-Centered Intelligent Realities
Funder
Knowledge Foundation, 20220068Available from: 2026-01-09 Created: 2026-01-09 Last updated: 2026-02-27Bibliographically approved

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Garro, ValeriaJusufi, IlirAbghari, ShahroozBoeva, Veselka

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