DROPS

Document

RATE-Analytics: Next Generation Predictive Analytics for Data-Driven Banking and Insurance

Authors: Dennis Collaris, Mykola Pechenizkiy, and Jarke J. van Wijk

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

Abstract

We conducted the RATE-Analytics project: a unique collaboration between Rabobank, Achmea, Tilburg and Eindhoven University. We aimed to develop foundations and techniques for next generation big data analytics. The main challenge of existing approaches is the lack of reliability and trustworthiness: if experts do not trust a model or its predictions they are much less likely to use and rely on that model. Hence, we focused on solutions to bring the human-in-the-loop, enabling the diagnostics and refinement of models, and support in decision making and justification. This chapter zooms in on the part of the project focused on developing explainable and trustworthy machine learning techniques.

Cite as

Dennis Collaris, Mykola Pechenizkiy, and Jarke J. van Wijk. RATE-Analytics: Next Generation Predictive Analytics for Data-Driven Banking and Insurance. In Commit2Data. Open Access Series in Informatics (OASIcs), Volume 124, pp. 8:1-8:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

Copy BibTex To Clipboard

@InProceedings{collaris_et_al:OASIcs.Commit2Data.8,
  author =	{Collaris, Dennis and Pechenizkiy, Mykola and van Wijk, Jarke J.},
  title =	{{RATE-Analytics: Next Generation Predictive Analytics for Data-Driven Banking and Insurance}},
  booktitle =	{Commit2Data},
  pages =	{8:1--8:11},
  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.8},
  URN =		{urn:nbn:de:0030-drops-213655},
  doi =		{10.4230/OASIcs.Commit2Data.8},
  annote =	{Keywords: Visualization, Visual Analytics, Machine Learning, Interpretability, Explainability, XAI}
}

Document

DOI: 10.4230/DagRep.2.9.131

Biological Data Visualization (Dagstuhl Seminar 12372)

Authors: Carsten Görg, Lawrence Hunter, Jessie Kennedy, Sean O'Donoghue, and Jarke J. Van Wijk

Published in: Dagstuhl Reports, Volume 2, Issue 9 (2013)

Abstract

The topic of visualizing biological data has recently seen growing interest. Visualization approaches can help researchers understand and analyze today's large and complex biological datasets. The aim of this seminar was to bring together biologists, bioinformaticians, and computer scientists to survey the current state of tools for visualizing biological data and to define a research agenda for developing the next generation of tools. During the seminar, the participants formed working groups on nine different topics, reflected on the ongoing research in those areas, and discussed how to address key challenges; six talks complemented the work in the breakout groups. This report documents the program and the outcome of Dagstuhl Seminar 12372 "Biological Data Visualization".

Cite as

Carsten Görg, Lawrence Hunter, Jessie Kennedy, Sean O'Donoghue, and Jarke J. Van Wijk. Biological Data Visualization (Dagstuhl Seminar 12372). In Dagstuhl Reports, Volume 2, Issue 9, pp. 131-164, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

Copy BibTex To Clipboard

@Article{gorg_et_al:DagRep.2.9.131,
  author =	{G\"{o}rg, Carsten and Hunter, Lawrence and Kennedy, Jessie and O'Donoghue, Sean and Van Wijk, Jarke J.},
  title =	{{Biological Data Visualization (Dagstuhl Seminar 12372)}},
  pages =	{131--164},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2013},
  volume =	{2},
  number =	{9},
  editor =	{G\"{o}rg, Carsten and Hunter, Lawrence and Kennedy, Jessie and O'Donoghue, Sean and Van Wijk, Jarke J.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.2.9.131},
  URN =		{urn:nbn:de:0030-drops-39731},
  doi =		{10.4230/DagRep.2.9.131},
  annote =	{Keywords: Information visualization, data visualization, biology, bioinformatics, user interfaces, visual analytics}
}

Search Results

Documents authored by Van Wijk, Jarke J.

RATE-Analytics: Next Generation Predictive Analytics for Data-Driven Banking and Insurance

Abstract

Cite as

Biological Data Visualization (Dagstuhl Seminar 12372)

Abstract

Cite as

Thanks for your feedback!

Could not send message