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Documents authored by Keim, Daniel A.

Found 2 Possible Name Variants:

Keim, Daniel A.

Document
DOI: 10.4230/DagRep.12.8.103
Interactive Visualization for Fostering Trust in ML (Dagstuhl Seminar 22351)

Authors: Polo Chau, Alex Endert, Daniel A. Keim, and Daniela Oelke

Published in: Dagstuhl Reports, Volume 12, Issue 8 (2023)

Abstract
The use of artificial intelligence continues to impact a broad variety of domains, application areas, and people. However, interpretability, understandability, responsibility, accountability, and fairness of the algorithms' results - all crucial for increasing humans' trust into the systems - are still largely missing. The purpose of this seminar is to understand how these components factor into the holistic view of trust. Further, this seminar seeks to identify design guidelines and best practices for how to build interactive visualization systems to calibrate trust.
Cite as

Polo Chau, Alex Endert, Daniel A. Keim, and Daniela Oelke. Interactive Visualization for Fostering Trust in ML (Dagstuhl Seminar 22351). In Dagstuhl Reports, Volume 12, Issue 8, pp. 103-116, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{chau_et_al:DagRep.12.8.103,
  author =	{Chau, Polo and Endert, Alex and Keim, Daniel A. and Oelke, Daniela},
  title =	{{Interactive Visualization for Fostering Trust in ML (Dagstuhl Seminar 22351)}},
  pages =	{103--116},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2023},
  volume =	{12},
  number =	{8},
  editor =	{Chau, Polo and Endert, Alex and Keim, Daniel A. and Oelke, Daniela},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.8.103},
  URN =		{urn:nbn:de:0030-drops-177161},
  doi =		{10.4230/DagRep.12.8.103},
  annote =	{Keywords: accountability, artificial intelligence, explainability, fairness, interactive visualization, machine learning, responsibility, trust, understandability}
}
Document
DOI: 10.4230/DagRep.10.4.37
Interactive Visualization for Fostering Trust in AI (Dagstuhl Seminar 20382)

Authors: Daniela Oelke, Daniel A. Keim, Polo Chau, and Alex Endert

Published in: Dagstuhl Reports, Volume 10, Issue 4 (2021)

Abstract
Artificial intelligence (AI), and in particular machine learning algorithms, are of increasing importance in many application areas but interpretability and understandability as well as responsibility, accountability, and fairness of the algorithms' results, all crucial for increasing the humans' trust into the systems, are still largely missing. Big industrial players, including Google, Microsoft, and Apple, have become aware of this gap and recently published their own guidelines for the use of AI in order to promote fairness, trust, interpretability, and other goals. Interactive visualization is one of the technologies that may help to increase trust in AI systems. During the seminar, we discussed the requirements for trustworthy AI systems as well as the technological possibilities provided by interactive visualizations to increase human trust in AI.
Cite as

Daniela Oelke, Daniel A. Keim, Polo Chau, and Alex Endert. Interactive Visualization for Fostering Trust in AI (Dagstuhl Seminar 20382). In Dagstuhl Reports, Volume 10, Issue 4, pp. 37-42, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@Article{oelke_et_al:DagRep.10.4.37,
  author =	{Oelke, Daniela and Keim, Daniel A. and Chau, Polo and Endert, Alex},
  title =	{{Interactive Visualization for Fostering Trust in AI (Dagstuhl Seminar 20382)}},
  pages =	{37--42},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2021},
  volume =	{10},
  number =	{4},
  editor =	{Oelke, Daniela and Keim, Daniel A. and Chau, Polo and Endert, Alex},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.10.4.37},
  URN =		{urn:nbn:de:0030-drops-137360},
  doi =		{10.4230/DagRep.10.4.37},
  annote =	{Keywords: accountability, artificial intelligence, explainability, fairness, interactive visualization, machine learning, responsibility, trust, understandability}
}
Document
DOI: 10.4230/DagRep.5.3.1
Bridging Information Visualization with Machine Learning (Dagstuhl Seminar 15101)

Authors: Daniel A. Keim, Tamara Munzner, Fabrice Rossi, and Michael Verleysen

Published in: Dagstuhl Reports, Volume 5, Issue 3 (2015)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 15101 "Bridging Information Visualization with Machine Learning". This seminar is a successor to Dagstuhl seminar 12081 "Information Visualization, Visual Data Mining and Machine Learning" held in 2012. The main goal of this second seminar was to identify important challenges to overcome in order to build systems that integrate machine learning and information visualization.
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Daniel A. Keim, Tamara Munzner, Fabrice Rossi, and Michael Verleysen. Bridging Information Visualization with Machine Learning (Dagstuhl Seminar 15101). In Dagstuhl Reports, Volume 5, Issue 3, pp. 1-27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)

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@Article{keim_et_al:DagRep.5.3.1,
  author =	{Keim, Daniel A. and Munzner, Tamara and Rossi, Fabrice and Verleysen, Michael},
  title =	{{Bridging Information Visualization with Machine Learning (Dagstuhl Seminar 15101)}},
  pages =	{1--27},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2015},
  volume =	{5},
  number =	{3},
  editor =	{Keim, Daniel A. and Munzner, Tamara and Rossi, Fabrice and Verleysen, Michael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.5.3.1},
  URN =		{urn:nbn:de:0030-drops-52665},
  doi =		{10.4230/DagRep.5.3.1},
  annote =	{Keywords: Information visualization, Machine learning, Visual data mining, Exploratory data analysis}
}
Document
DOI: 10.4230/DagRep.2.2.58
Information Visualization, Visual Data Mining and Machine Learning (Dagstuhl Seminar 12081)

Authors: Daniel A. Keim, Fabrice Rossi, Thomas Seidl, Michel Verleysen, and Stefan Wrobel

Published in: Dagstuhl Reports, Volume 2, Issue 2 (2012)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 12081 ``Information Visualization, Visual Data Mining and Machine Learning''. The aim of the seminar was to tighten the links between the information visualisation community and the machine learning community in order to explore how each field can benefit from the other and how to go beyond current hybridization successes.
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Daniel A. Keim, Fabrice Rossi, Thomas Seidl, Michel Verleysen, and Stefan Wrobel. Information Visualization, Visual Data Mining and Machine Learning (Dagstuhl Seminar 12081). In Dagstuhl Reports, Volume 2, Issue 2, pp. 58-83, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

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@Article{keim_et_al:DagRep.2.2.58,
  author =	{Keim, Daniel A. and Rossi, Fabrice and Seidl, Thomas and Verleysen, Michel and Wrobel, Stefan},
  title =	{{Information Visualization, Visual Data Mining and Machine Learning (Dagstuhl Seminar 12081)}},
  pages =	{58--83},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2012},
  volume =	{2},
  number =	{2},
  editor =	{Keim, Daniel A. and Rossi, Fabrice and Seidl, Thomas and Verleysen, Michel and Wrobel, Stefan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.2.2.58},
  URN =		{urn:nbn:de:0030-drops-35064},
  doi =		{10.4230/DagRep.2.2.58},
  annote =	{Keywords: Information visualization, visual data mining, machine learning, nonlinear dimensionality reduction, exploratory data analysis}
}
Document
DOI: 10.4230/DagSemProc.10471.1
10471 Abstracts Collection – Scalable Visual Analytics

Authors: Daniel A. Keim and Stefan Wrobel

Published in: Dagstuhl Seminar Proceedings, Volume 10471, Scalable Visual Analytics (2011)

Abstract
From 21.11. to 26.11.2010, the Dagstuhl Seminar 10471 ``Scalable Visual Analytics'' was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
Cite as

Daniel A. Keim and Stefan Wrobel. 10471 Abstracts Collection – Scalable Visual Analytics. In Scalable Visual Analytics. Dagstuhl Seminar Proceedings, Volume 10471, pp. 1-16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)

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@InProceedings{keim_et_al:DagSemProc.10471.1,
  author =	{Keim, Daniel A. and Wrobel, Stefan},
  title =	{{10471 Abstracts Collection – Scalable Visual Analytics}},
  booktitle =	{Scalable Visual Analytics},
  pages =	{1--16},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2011},
  volume =	{10471},
  editor =	{Daniel A. Keim and Stefan Wrobel},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10471.1},
  URN =		{urn:nbn:de:0030-drops-29406},
  doi =		{10.4230/DagSemProc.10471.1},
  annote =	{Keywords: Visual Analytics, Visualization, Data Analysis, Discovery Science, Information Visualization}
}
Document
DOI: 10.4230/DagSemProc.10471.2
10471 Executive Summary – Scalable Visual Analytics

Authors: Daniel A. Keim and Stefan Wrobel

Published in: Dagstuhl Seminar Proceedings, Volume 10471, Scalable Visual Analytics (2011)

Abstract
The Scalable Visual Analytics seminar was a fertile meeting in which researchers from diverse backgrounds met. It included industry and academia, senior and junior researchers, multi-national representation, and people coming from several disciplines. The diversity resulted in interesting and useful discussions, which will help to shape the future of the versatile research area of Visual Analytics. The seminar included multiple presentations and discussions which helped to exchange domain knowledge and steer future research activities. Besides, several working groups during the seminar not only identified future research directions in the field of scalable visual analytics but also initiated new joint projects. In total, plans for three position papers, two overview papers to outreach to other communities, and three EU FET Open Projects were drafted. Furthermore, three workshops as satellites of conferences that cover specific application areas were planned to further disseminate the work and provide a platform for ongoing discussions and activities.
Cite as

Daniel A. Keim and Stefan Wrobel. 10471 Executive Summary – Scalable Visual Analytics. In Scalable Visual Analytics. Dagstuhl Seminar Proceedings, Volume 10471, pp. 1-5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)

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@InProceedings{keim_et_al:DagSemProc.10471.2,
  author =	{Keim, Daniel A. and Wrobel, Stefan},
  title =	{{10471 Executive Summary – Scalable Visual Analytics}},
  booktitle =	{Scalable Visual Analytics},
  pages =	{1--5},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2011},
  volume =	{10471},
  editor =	{Daniel A. Keim and Stefan Wrobel},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10471.2},
  URN =		{urn:nbn:de:0030-drops-29393},
  doi =		{10.4230/DagSemProc.10471.2},
  annote =	{Keywords: Visual Analytics, Visualization, Data Analysis, Discovery Science, Information Visualization}
}
Document
DOI: 10.4230/DFU.SciViz.2010.227
From Visualization to Visually Enabled Reasoning

Authors: Joerg Meyer, Jim Thomas, Stephan Diehl, Brian Fisher, and Daniel A. Keim

Published in: Dagstuhl Follow-Ups, Volume 1, Scientific Visualization: Advanced Concepts (2010)

Abstract
Interactive Visualization has been used to study scientific phenomena, analyze data, visualize information, and to explore large amounts of multi-variate data. It enables the human mind to gain novel insights by empowering the human visual system, encompassing the brain and the eyes, to discover properties that were previously unknown. While it is believed that the process of creating interactive visualizations is reasonably well understood, the process of stimulating and enabling human reasoning with the aid of interactive visualization tools is still a highly unexplored field. We hypothesize that visualizations make an impact if they successfully influence a thought process or a decision. Interacting with visualizations is part of this process. We present exemplary cases where visualization was successful in enabling human reasoning, and instances where the interaction with data helped in understanding the data and making a better informed decision. We suggest metrics that help in understanding the evolution of a decision making process. Such a metric would measure the efficiency of the reasoning process, rather than the performance of the visualization system or the user. We claim that the methodology of interactive visualization, which has been studied to a great extent, is now sufficiently mature, and we would like to provide some guidance regarding the evaluation of knowledge gain through visually enabled reasoning. It is our ambition to encourage the reader to take on the next step and move from information visualization to visually enabled reasoning.
Cite as

Joerg Meyer, Jim Thomas, Stephan Diehl, Brian Fisher, and Daniel A. Keim. From Visualization to Visually Enabled Reasoning. In Scientific Visualization: Advanced Concepts. Dagstuhl Follow-Ups, Volume 1, pp. 227-245, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InCollection{meyer_et_al:DFU.SciViz.2010.227,
  author =	{Meyer, Joerg and Thomas, Jim and Diehl, Stephan and Fisher, Brian and Keim, Daniel A.},
  title =	{{From Visualization to Visually Enabled Reasoning}},
  booktitle =	{Scientific Visualization: Advanced Concepts},
  pages =	{227--245},
  series =	{Dagstuhl Follow-Ups},
  ISBN =	{978-3-939897-19-4},
  ISSN =	{1868-8977},
  year =	{2010},
  volume =	{1},
  editor =	{Hagen, Hans},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DFU.SciViz.2010.227},
  URN =		{urn:nbn:de:0030-drops-27078},
  doi =		{10.4230/DFU.SciViz.2010.227},
  annote =	{Keywords: Interactive Visualization, Reasoning}
}
Document
DOI: 10.4230/DagSemProc.09211.1
09211 Abstracts Collection – Visualization and Monitoring of Network Traffic

Authors: Daniel A. Keim, Aiko Pras, Jürgen Schönwälder, and Pak Chung Wong

Published in: Dagstuhl Seminar Proceedings, Volume 9211, Visualization and Monitoring of Network Traffic (2009)

Abstract
From 17.05. to 20.05.2009, the Dagstuhl Seminar 09211 ``Visualization and Monitoring of Network Traffic '' was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
Cite as

Daniel A. Keim, Aiko Pras, Jürgen Schönwälder, and Pak Chung Wong. 09211 Abstracts Collection – Visualization and Monitoring of Network Traffic. In Visualization and Monitoring of Network Traffic. Dagstuhl Seminar Proceedings, Volume 9211, pp. 1-8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{keim_et_al:DagSemProc.09211.1,
  author =	{Keim, Daniel A. and Pras, Aiko and Sch\"{o}nw\"{a}lder, J\"{u}rgen and Wong, Pak Chung},
  title =	{{09211 Abstracts Collection – Visualization and Monitoring of Network Traffic }},
  booktitle =	{Visualization and Monitoring of Network Traffic},
  pages =	{1--8},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9211},
  editor =	{Daniel A. Keim and Aiko Pras and J\"{u}rgen Sch\"{o}nw\"{a}lder and Pak Chung Wong},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09211.1},
  URN =		{urn:nbn:de:0030-drops-21586},
  doi =		{10.4230/DagSemProc.09211.1},
  annote =	{Keywords: Computer Networks, Internet, Monitoring of Networks and Services, Visualization Animation}
}
Document
DOI: 10.4230/DagSemProc.09211.2
09211 Executive Summary – Visualization and Monitoring of Network Traffic

Authors: Daniel A. Keim, Aiko Pras, Jürgen Schönwälder, Pak Chung Wong, and Florian Mansmann

Published in: Dagstuhl Seminar Proceedings, Volume 9211, Visualization and Monitoring of Network Traffic (2009)

Abstract
The seamless operation of the Internet requires being able to monitor and visualize the actual behaviour of the network. Today, IP network operators usually collect network flow statistics from critical points of their network infrastructure. Flows aggregate packets that share common properties. Flow records are stored and analyzed to extract accounting information and increasingly to identify and isolate network problems or security incidents. While network problems or attacks significantly changing traffic patterns are relatively easy to identify, it tends to be much more challenging to identify creeping changes or attacks and faults that manifest themselves only by very careful analysis of initially seemingly unrelated traffic pattern and their changes. There are currently no deployable good solutions and research in this area is just starting. In addition, the large volume of flow data on high capacity networks and exchange points requires to move to probabilistic sampling techniques, which require new analysis techniques to calculate and also visualize the uncertainty attached to data sets.
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Daniel A. Keim, Aiko Pras, Jürgen Schönwälder, Pak Chung Wong, and Florian Mansmann. 09211 Executive Summary – Visualization and Monitoring of Network Traffic. In Visualization and Monitoring of Network Traffic. Dagstuhl Seminar Proceedings, Volume 9211, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{keim_et_al:DagSemProc.09211.2,
  author =	{Keim, Daniel A. and Pras, Aiko and Sch\"{o}nw\"{a}lder, J\"{u}rgen and Wong, Pak Chung and Mansmann, Florian},
  title =	{{09211 Executive Summary – Visualization and Monitoring of Network Traffic}},
  booktitle =	{Visualization and Monitoring of Network Traffic},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9211},
  editor =	{Daniel A. Keim and Aiko Pras and J\"{u}rgen Sch\"{o}nw\"{a}lder and Pak Chung Wong},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09211.2},
  URN =		{urn:nbn:de:0030-drops-21574},
  doi =		{10.4230/DagSemProc.09211.2},
  annote =	{Keywords: Computer Networks, Internet, Monitoring of Networks and Services, Visualization Animation}
}
Document
DOI: 10.4230/DagSemProc.07291.1
07291 Abstracts Collection – Scientific Visualization

Authors: David S. Ebert, Hans Hagen, Kenneth I. Joy, and Daniel A. Keim

Published in: Dagstuhl Seminar Proceedings, Volume 7291, Scientific Visualization (2008)

Abstract
From 15.07. to 20.07.07, the Dagstuhl Seminar 07291 ``Scientific Visualization'' was held in the International Conference and Research Center (IBFI),Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
Cite as

David S. Ebert, Hans Hagen, Kenneth I. Joy, and Daniel A. Keim. 07291 Abstracts Collection – Scientific Visualization. In Scientific Visualization. Dagstuhl Seminar Proceedings, Volume 7291, pp. 1-18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{ebert_et_al:DagSemProc.07291.1,
  author =	{Ebert, David S. and Hagen, Hans and Joy, Kenneth I. and Keim, Daniel A.},
  title =	{{07291 Abstracts Collection – Scientific Visualization}},
  booktitle =	{Scientific Visualization},
  pages =	{1--18},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{7291},
  editor =	{David S. Ebert and Hans Hagen and Kenneth I. Joy and Daniel A. Keim},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07291.1},
  URN =		{urn:nbn:de:0030-drops-14145},
  doi =		{10.4230/DagSemProc.07291.1},
  annote =	{Keywords: Markov chains, numerical methods, web information retrieval, performance evaluation, intrusion detection, aggregation-disaggregation methods, graph-oriented decomposition}
}
Document
DOI: 10.4230/DagSemProc.07291.2
07291 Summary – Scientific Visualization

Authors: David S. Ebert, Hans Hagen, Kenneth I. Joy, and Daniel A. Keim

Published in: Dagstuhl Seminar Proceedings, Volume 7291, Scientific Visualization (2008)

Abstract
Scientific visualization (SV) is concerned with the use of computer-generated images to aid the understanding, analysis and manipulation of data. Since its beginning in the early 90's, the techniques of SV have aided scientists, engineers, medical practitioners, and others in the study of a wide variety of data sets including, for example, high performance computing simulations, measured data from scanners (CAT, MR, confocal microscopy), internet traffic, and financial records. One of the important themes being nurtured under the aegis of Scientific Visualization is the utilization of the broad bandwidth of the human sensory system in steering and interpreting complex processes and simulations involving voluminous data sets across diverse scientific disciplines. Since vision dominates our sensory input, strong efforts have been made to bring the mathematical abstraction and modeling to our eyes through the mediation of computer graphics. This interplay between various application areas and their specific problem solving visualization techniques was emphasized in the proposed seminar.
Cite as

David S. Ebert, Hans Hagen, Kenneth I. Joy, and Daniel A. Keim. 07291 Summary – Scientific Visualization. In Scientific Visualization. Dagstuhl Seminar Proceedings, Volume 7291, pp. 1-2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{ebert_et_al:DagSemProc.07291.2,
  author =	{Ebert, David S. and Hagen, Hans and Joy, Kenneth I. and Keim, Daniel A.},
  title =	{{07291 Summary – Scientific Visualization}},
  booktitle =	{Scientific Visualization},
  pages =	{1--2},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{7291},
  editor =	{David S. Ebert and Hans Hagen and Kenneth I. Joy and Daniel A. Keim},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07291.2},
  URN =		{urn:nbn:de:0030-drops-14132},
  doi =		{10.4230/DagSemProc.07291.2},
  annote =	{Keywords: Markov chains, numerical methods, web information retrieval, performance evaluation, intrusion detection, aggregation-disaggregation methods graph-oriented decomposition}
}

Keim, Daniel

Document
DOI: 10.4230/DagSemProc.04292.1
Data Mining: The Next Generation

Authors: Raghu Ramakrishnan, Rakesh Agrawal, Johann-Christoph Freytag, Toni Bollinger, Christopher W. Clifton, Saso Dzeroski, Jochen Hipp, Daniel Keim, Stefan Kramer, Hans-Peter Kriegel, Ulf Leser, Bing Liu, Heikki Mannila, Rosa Meo, Shinichi Morishita, Raymond Ng, Jian Pei, Prabhakar Raghavan, Myra Spiliopoulou, Jaideep Srivastava, and Vicenc Torra

Published in: Dagstuhl Seminar Proceedings, Volume 4292, Perspectives Workshop: Data Mining: The Next Generation (2005)

Abstract
Data Mining (DM) has enjoyed great popularity in recent years, with advances in both research and commercialization. The first generation of DM research and development has yielded several commercially available systems, both stand-alone and integrated with database systems; produced scalable versions of algorithms for many classical DM problems; and introduced novel pattern discovery problems. In recent years, research has tended to be fragmented into several distinct pockets without a comprehensive framework. Researchers have continued to work largely within the parameters of their parent disciplines, building upon existing and distinct research methodologies. Even when they address a common problem (for example, how to cluster a dataset) they apply different techniques, different perspectives on what the important issues are, and different evaluation criteria. While different approaches can be complementary, and such a diversity is ultimately a strength of the field, better communication across disciplines is required if DM is to forge a distinct identity with a core set of principles, perspectives, and challenges that differentiate it from each of the parent disciplines. Further, while the amount and complexity of data continues to grow rapidly, and the task of distilling useful insight continues to be central, serious concerns have emerged about social implications of DM. Addressing these concerns will require advances in our theoretical understanding of the principles that underlie DM algorithms, as well as an integrated approach to security and privacy in all phases of data management and analysis. Researchers from a variety of backgrounds assembled at Dagstuhl to re-assess the current directions of the field, to identify critical problems that require attention, and to discuss ways to increase the flow of ideas across the different disciplines that DM has brought together. The workshop did not seek to draw up an agenda for the field of DM. Rather, it offers the participants’ perspective on two technical directions – compositionality and privacy – and describes some important application challenges that drove the discussion. Both of these directions illustrate the opportunities for crossdisciplinary research, and there was broad agreement that they represent important and timely areas for further work; of course, the choice of these directions as topics for discussion also reflects the personal interests and biases of the workshop participants.
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Raghu Ramakrishnan, Rakesh Agrawal, Johann-Christoph Freytag, Toni Bollinger, Christopher W. Clifton, Saso Dzeroski, Jochen Hipp, Daniel Keim, Stefan Kramer, Hans-Peter Kriegel, Ulf Leser, Bing Liu, Heikki Mannila, Rosa Meo, Shinichi Morishita, Raymond Ng, Jian Pei, Prabhakar Raghavan, Myra Spiliopoulou, Jaideep Srivastava, and Vicenc Torra. Data Mining: The Next Generation. In Perspectives Workshop: Data Mining: The Next Generation. Dagstuhl Seminar Proceedings, Volume 4292, pp. 1-33, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005)

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@InProceedings{ramakrishnan_et_al:DagSemProc.04292.1,
  author =	{Ramakrishnan, Raghu and Agrawal, Rakesh and Freytag, Johann-Christoph and Bollinger, Toni and Clifton, Christopher W. and Dzeroski, Saso and Hipp, Jochen and Keim, Daniel and Kramer, Stefan and Kriegel, Hans-Peter and Leser, Ulf and Liu, Bing and Mannila, Heikki and Meo, Rosa and Morishita, Shinichi and Ng, Raymond and Pei, Jian and Raghavan, Prabhakar and Spiliopoulou, Myra and Srivastava, Jaideep and Torra, Vicenc},
  title =	{{Data Mining: The Next Generation}},
  booktitle =	{Perspectives Workshop: Data Mining: The Next Generation},
  pages =	{1--33},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2005},
  volume =	{4292},
  editor =	{Rakesh Agrawal and Johann Christoph Freytag and Raghu Ramakrishnan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.04292.1},
  URN =		{urn:nbn:de:0030-drops-2709},
  doi =		{10.4230/DagSemProc.04292.1},
  annote =	{Keywords: Data mining, databases, artificial intelligence, machine learning, statistics, semantics}
}
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