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Documents authored by van Binsbergen, L. Thomas

Document
DOI: 10.4230/OASIcs.Programming.2025.6
Exploration and Complexity Management in Graph-Based Programming Environments

Authors: Max Boksem and L. Thomas van Binsbergen

Published in: OASIcs, Volume 134, Companion Proceedings of the 9th International Conference on the Art, Science, and Engineering of Programming (Programming 2025)

Abstract
Programmers often rely on different environments depending on the nature of their tasks. For large-scale software projects, IDEs help manage complexity through structured abstractions like files, modules, and classes, and provide tools for code visualization and navigation. In contrast, exploratory programming tasks - such as data analysis, rapid prototyping, and design space exploration - are better served by interactive environments like REPLs and Notebooks, which support incremental development and immediate feedback. However, these tools tend to prioritize either complexity management or exploration, limiting their effectiveness across contexts. This paper investigates a hybrid graph-based programming environment that bridges these two modes by building on Incremental Graph Code (IGC), a graph-based system for structuring, visualizing, and interacting with source code. We explore how IGC can support both complexity management and exploratory programming through three key features: projectional views for aggregating and navigating interrelated code and documentation, graph-type nodes for encapsulating subgraphs to manage structural complexity, and an exploratory programming view for managing branching executions and promoting experimentation. Together, these features suggest that graph-based environments like IGC can offer a unified platform for both systematic software engineering and dynamic, exploratory development.
Cite as

Max Boksem and L. Thomas van Binsbergen. Exploration and Complexity Management in Graph-Based Programming Environments. In Companion Proceedings of the 9th International Conference on the Art, Science, and Engineering of Programming (Programming 2025). Open Access Series in Informatics (OASIcs), Volume 134, pp. 6:1-6:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{boksem_et_al:OASIcs.Programming.2025.6,
  author =	{Boksem, Max and van Binsbergen, L. Thomas},
  title =	{{Exploration and Complexity Management in Graph-Based Programming Environments}},
  booktitle =	{Companion Proceedings of the 9th International Conference on the Art, Science, and Engineering of Programming (Programming 2025)},
  pages =	{6:1--6:18},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-382-9},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{134},
  editor =	{Edwards, Jonathan and Perera, Roly and Petricek, Tomas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Programming.2025.6},
  URN =		{urn:nbn:de:0030-drops-242906},
  doi =		{10.4230/OASIcs.Programming.2025.6},
  annote =	{Keywords: Graph-based Programming Environments, Exploratory Programming, Complexity Management, Incremental Graph Code (IGC), Projectional Views}
}
Document
DOI: 10.4230/OASIcs.Commit2Data.2
Building a Digital Health Twin for Personalized Intervention: The EPI Project

Authors: Jamila Alsayed Kassem, Corinne Allaart, Saba Amiri, Milen Kebede, Tim Müller, Rosanne Turner, Adam Belloum, L. Thomas van Binsbergen, Peter Grunwald, Aart van Halteren, Paola Grosso, Cees de Laat, and Sander Klous

Published in: OASIcs, Volume 124, Commit2Data (2024)

Abstract
The Enabling Personalized Interventions (EPI) project, part of the COMMIT2DATA top sector initiative, brings together research on data science, software-defined network infrastructure, and secure and trustworthy data sharing, executed within the healthcare domain. The project applies the digital twin paradigm, in which data science-driven algorithms monitor and perform functions on a digital counterpart of a real-world entity, to enable proactive responses based on predicted outcomes. The EPI project applies this paradigm in the healthcare context by developing and testing applications that can act as personalized digital health twins for self/-joint management. The EPI project addresses several challenges to digital twin applications in the healthcare domain, such as: 1) strict health data sharing policies often lead to data being locked in silos, 2) legal, policy and privacy requirements make data processing increasingly more complex, and 3) significant limitations on infrastructure resources may apply. In this paper, we report on the use cases the EPI used as the basis to develop possible solutions to these challenges. In particular, we describe algorithms and tools for algorithmic real-time response and analysis of distributed data at scale. We discuss the automatic enforcement of legal interpretations and data-sharing conditions as executable policies. Finally, we investigate infrastructural challenges by implementing and experimenting with the EPI Framework - consisting of a distributed analysis infrastructure and BRANE for orchestrating multi-site applications. We conclude by describing our Proof of Concept (PoC) and showing its application to one of the EPI use cases.
Cite as

Jamila Alsayed Kassem, Corinne Allaart, Saba Amiri, Milen Kebede, Tim Müller, Rosanne Turner, Adam Belloum, L. Thomas van Binsbergen, Peter Grunwald, Aart van Halteren, Paola Grosso, Cees de Laat, and Sander Klous. Building a Digital Health Twin for Personalized Intervention: The EPI Project. In Commit2Data. Open Access Series in Informatics (OASIcs), Volume 124, pp. 2:1-2:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{alsayedkassem_et_al:OASIcs.Commit2Data.2,
  author =	{Alsayed Kassem, Jamila and Allaart, Corinne and Amiri, Saba and Kebede, Milen and M\"{u}ller, Tim and Turner, Rosanne and Belloum, Adam and van Binsbergen, L. Thomas and Grunwald, Peter and van Halteren, Aart and Grosso, Paola and de Laat, Cees and Klous, Sander},
  title =	{{Building a Digital Health Twin for Personalized Intervention: The EPI Project}},
  booktitle =	{Commit2Data},
  pages =	{2:1--2:18},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-351-5},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{124},
  editor =	{Haverkort, Boudewijn R. and de Jongste, Aldert and van Kuilenburg, Pieter and Vromans, Ruben D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Commit2Data.2},
  URN =		{urn:nbn:de:0030-drops-213596},
  doi =		{10.4230/OASIcs.Commit2Data.2},
  annote =	{Keywords: Healthcare, Data Sharing, Personalised Medicine, Real-time Data Analysis, Digital Health Twin, Data Policies}
}
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