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Dagstuhl Reports, Volume 8, Issue 10

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Dagstuhl Seminars 18411, 18412, 18421, 18422, 18431, 18441, 18442

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Document
Complete Issue
DOI: 10.4230/DagRep.8.10
Dagstuhl Reports, Volume 8, Issue 10, October 2018, Complete Issue
Abstract
Dagstuhl Reports, Volume 8, Issue 10, October 2018, Complete Issue
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Dagstuhl Reports, Volume 8, Issue 10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@Article{DagRep.8.10,
  title =	{{Dagstuhl Reports, Volume 8, Issue 10, October 2018, Complete Issue}},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2019},
  volume =	{8},
  number =	{10},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.8.10},
  URN =		{urn:nbn:de:0030-drops-105627},
  doi =		{10.4230/DagRep.8.10},
  annote =	{Keywords: Dagstuhl Reports, Volume 8, Issue 10, October 2018, Complete Issue}
}
Document
Front Matter
DOI: 10.4230/DagRep.8.10.i
Dagstuhl Reports, Table of Contents, Volume 8, Issue 10, 2018
Abstract
Dagstuhl Reports, Table of Contents, Volume 8, Issue 10, 2018
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Dagstuhl Reports, Volume 8, Issue 10, pp. i-ii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@Article{DagRep.8.10.i,
  title =	{{Dagstuhl Reports, Table of Contents, Volume 8, Issue 10, 2018}},
  pages =	{i--ii},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2019},
  volume =	{8},
  number =	{10},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.8.10.i},
  URN =		{urn:nbn:de:0030-drops-105618},
  doi =		{10.4230/DagRep.8.10.i},
  annote =	{Keywords: Table of Contents, Frontmatter}
}
Document
DOI: 10.4230/DagRep.8.10.1
Progressive Data Analysis and Visualization (Dagstuhl Seminar 18411)

Authors: Jean-Daniel Fekete, Danyel Fisher, Arnab Nandi, and Michael Sedlmair

Abstract
We live in an era where data is abundant and growing rapidly; databases storing big data sprawl past memory and computation limits, and across distributed systems. New hardware and software systems have been built to sustain this growth in terms of storage management and predictive computation. However, these infrastructures, while good for data at scale, do not well support exploratory data analysis (EDA) as, for instance, commonly used in Visual Analytics. EDA allows human users to make sense of data with little or no known model on this data and is essential in many application domains, from network security and fraud detection to epidemiology and preventive medicine. Data exploration is done through an iterative loop where analysts interact with data through computations that return results, usually shown with visualizations, which in turn are interacted with by the analyst again. Due to human cognitive constraints, exploration needs highly responsive system response times: at 500 ms, users change their querying behavior; past five or ten seconds, users abandon tasks or lose attention. As datasets grow and computations become more complex, response time suffers. To address this problem, a new computation paradigm has emerged in the last decade under several names: online aggregation in the database community; progressive, incremental, or iterative visualization in other communities. It consists of splitting long computations into a series of approximate results improving with time; in this process, partial or approximate results are then rapidly returned to the user and can be interacted with in a fluent and iterative fashion. With the increasing growth in data, such progressive data analysis approaches will become one of the leading paradigms for data exploration systems, but it also will require major changes in the algorithms, data structures, and visualization tools. This Dagstuhl Seminar was set out to discuss and address these challenges, by bringing together researchers from the different involved research communities: database, visualization, and machine learning. Thus far, these communities have often been divided by a gap hindering joint efforts in dealing with forthcoming challenges in progressive data analysis and visualization. The seminar gave a platform for these researchers and practitioners to exchange their ideas, experience, and visions, jointly develop strategies to deal with challenges, and create a deeper awareness of the implications of this paradigm shift. The implications are technical, but also human--both perceptual and cognitive--and the seminar provided a holistic view of the problem by gathering specialists from all the communities.
Cite as

Jean-Daniel Fekete, Danyel Fisher, Arnab Nandi, and Michael Sedlmair. Progressive Data Analysis and Visualization (Dagstuhl Seminar 18411). In Dagstuhl Reports, Volume 8, Issue 10, pp. 1-40, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@Article{fekete_et_al:DagRep.8.10.1,
  author =	{Fekete, Jean-Daniel and Fisher, Danyel and Nandi, Arnab and Sedlmair, Michael},
  title =	{{Progressive Data Analysis and Visualization (Dagstuhl Seminar 18411)}},
  pages =	{1--40},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2019},
  volume =	{8},
  number =	{10},
  editor =	{Fekete, Jean-Daniel and Fisher, Danyel and Nandi, Arnab and Sedlmair, Michael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.8.10.1},
  URN =		{urn:nbn:de:0030-drops-103464},
  doi =		{10.4230/DagRep.8.10.1},
  annote =	{Keywords: Approximate Query Processing, Online Aggregation, Exploratory Data Analysis, Visual Analytics, Progressive Data Analysis, Scalability}
}
Document
DOI: 10.4230/DagRep.8.10.41
Encouraging Reproducibility in Scientific Research of the Internet (Dagstuhl Seminar 18412)

Authors: Vaibhav Bajpai, Olivier Bonaventure, Kimberly Claffy, and Daniel Karrenberg

Abstract
Reproducibility of research in Computer Science and in the field of networking in particular is a well-recognized problem. For several reasons, including the sensitive and/or proprietary nature of some Internet measurements, the networking research community pays limited attention to the of reproducibility of results, instead tending to accept papers that appear plausible. This article summarises a 2.5 day long Dagstuhl seminar on Encouraging Reproducibility in Scientific Research of the Internet held in October 2018. The seminar discussed challenges to improving reproducibility of scientific Internet research, and developed a set of recommendations that we as a community can undertake to initiate a cultural change toward reproducibility of our work. It brought together people both from academia and industry to set expectations and formulate concrete recommendations for reproducible research. This iteration of the seminar was scoped to computer networking research, although the outcomes are likely relevant for a broader audience from multiple interdisciplinary fields.
Cite as

Vaibhav Bajpai, Olivier Bonaventure, Kimberly Claffy, and Daniel Karrenberg. Encouraging Reproducibility in Scientific Research of the Internet (Dagstuhl Seminar 18412). In Dagstuhl Reports, Volume 8, Issue 10, pp. 41-62, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@Article{bajpai_et_al:DagRep.8.10.41,
  author =	{Bajpai, Vaibhav and Bonaventure, Olivier and Claffy, Kimberly and Karrenberg, Daniel},
  title =	{{Encouraging Reproducibility in Scientific Research of the Internet (Dagstuhl Seminar 18412)}},
  pages =	{41--62},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2019},
  volume =	{8},
  number =	{10},
  editor =	{Bajpai, Vaibhav and Bonaventure, Olivier and Claffy, Kimberly and Karrenberg, Daniel},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.8.10.41},
  URN =		{urn:nbn:de:0030-drops-103471},
  doi =		{10.4230/DagRep.8.10.41},
  annote =	{Keywords: Computer Networks, Reproducibility}
}
Document
DOI: 10.4230/DagRep.8.10.63
Algorithmic Enumeration: Output-sensitive, Input-Sensitive, Parameterized, Approximative (Dagstuhl Seminar 18421)

Authors: Henning Fernau, Petr. A. Golovach, and Marie-France Sagot

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 18421 "Algorithmic Enumeration: Output-sensitive, Input-Sensitive, Parameterized, Approximative". Enumeration problems require to list all wanted objects of the input as, e.g., particular subsets of the vertex or edge set of a given graph or particular satisfying assignments of logical expressions. Enumeration problems arise in a natural way in various fields of Computer Science, as, e.g., Artificial Intelligence and Data Mining, in Natural Sciences Engineering, Social Sciences, and Biology. The main challenge of the area of enumeration problems is that, contrary to decision, optimization and even counting problems, the output length of an enumeration problem is often exponential in the size of the input and cannot be neglected. This makes enumeration problems more challenging than many other types of algorithmic problems and demands development of specific techniques. The principal goals of our Dagstuhl seminar were to increase the visibility of algorithmic enumeration within (Theoretical) Computer Science and to contribute to establishing it as an area of ``Algorithms and Complexity''. The seminar brought together researchers within the algorithms community, other fields of Computer Science and Computer Engineering, as well as researchers working on enumeration problems in other application areas, in particular Biology. The aim was to accelerate developments and discus new directions including algorithmic tools and hardness proofs.
Cite as

Henning Fernau, Petr. A. Golovach, and Marie-France Sagot. Algorithmic Enumeration: Output-sensitive, Input-Sensitive, Parameterized, Approximative (Dagstuhl Seminar 18421). In Dagstuhl Reports, Volume 8, Issue 10, pp. 63-86, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@Article{fernau_et_al:DagRep.8.10.63,
  author =	{Fernau, Henning and Golovach, Petr. A. and Sagot, Marie-France},
  title =	{{Algorithmic Enumeration: Output-sensitive, Input-Sensitive, Parameterized, Approximative (Dagstuhl Seminar 18421)}},
  pages =	{63--86},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2019},
  volume =	{8},
  number =	{10},
  editor =	{Fernau, Henning and Golovach, Petr. A. and Sagot, Marie-France},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.8.10.63},
  URN =		{urn:nbn:de:0030-drops-103483},
  doi =		{10.4230/DagRep.8.10.63},
  annote =	{Keywords: algorithms, input-sensitive enumeration, output-sensitive enumeration}
}
Document
DOI: 10.4230/DagRep.8.10.87
Shape Analysis: Euclidean, Discrete and Algebraic Geometric Methods (Dagstuhl Seminar 18422)

Authors: Michael Breuß, Alfred M. Bruckstein, Christer Oscar Kiselman, and Petros Maragos

Abstract
In computer vision, geometric processing and image analysis, the notation of a shape of a 3-D object has been studied either by an embedded manifold for the continuous stetting or as a collection of a discrete set of marker positions on the manifold. Within the last years, there have been many rapid developments in the field of shape representation, shape correspondence and shape manipulation with technical applications ranging from laser-range scanners to 3-D printing. Classic shape analysis tools from differential geometry have a fresh influence in the field, often powered by modern methods from optimization and numerical computing. At the same time, discrete geometric methods and related techniques such as from mathematical morphology have evolved significantly. Moreover, techniques like deep learning gained a significant influence in the development of corresponding methods and tools. New developments from tropical geometry have a high potential for use in shape analysis. The topics in our seminar addressed the sketched challenges and developments that will be useful for shape analysis. Especially we aimed to discuss the possibilities of combining fields like tropical geometry with more classical techniques as for instance from mathematical morphology. We discussed possibilities of applying machine learning methods in this context and considered recent advances from more classical fields like differential geometry and partial differential equations that can be useful for setting up and understanding shape analysis methods in all of these approaches. This seminar brought together 26 researchers from North America and Europe engaged in recent and upcoming developments in shape analysis who view these challenges from different perspectives and who together discussed the pressing open problems and novel solutions to them.
Cite as

Michael Breuß, Alfred M. Bruckstein, Christer Oscar Kiselman, and Petros Maragos. Shape Analysis: Euclidean, Discrete and Algebraic Geometric Methods (Dagstuhl Seminar 18422). In Dagstuhl Reports, Volume 8, Issue 10, pp. 87-103, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@Article{breu_et_al:DagRep.8.10.87,
  author =	{Breu{\ss}, Michael and Bruckstein, Alfred M. and Kiselman, Christer Oscar and Maragos, Petros},
  title =	{{Shape Analysis: Euclidean, Discrete and Algebraic Geometric Methods (Dagstuhl Seminar 18422)}},
  pages =	{87--103},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2019},
  volume =	{8},
  number =	{10},
  editor =	{Breu{\ss}, Michael and Bruckstein, Alfred M. and Kiselman, Christer Oscar and Maragos, Petros},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.8.10.87},
  URN =		{urn:nbn:de:0030-drops-103499},
  doi =		{10.4230/DagRep.8.10.87},
  annote =	{Keywords: algebraic geometry, mathematical morphology, optimization, shape analysis, shape reconstruction}
}
Document
DOI: 10.4230/DagRep.8.10.104
Computational Aspects of Fabrication (Dagstuhl Seminar 18431)

Authors: Bernd Bickel, Marc Alexa, Jessica K. Hodgins, and Kristina Shea

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 18431 "Computational Aspects of Fabrication".
Cite as

Bernd Bickel, Marc Alexa, Jessica K. Hodgins, and Kristina Shea. Computational Aspects of Fabrication (Dagstuhl Seminar 18431). In Dagstuhl Reports, Volume 8, Issue 10, pp. 104-126, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@Article{bickel_et_al:DagRep.8.10.104,
  author =	{Bickel, Bernd and Alexa, Marc and Hodgins, Jessica K. and Shea, Kristina},
  title =	{{Computational Aspects of Fabrication (Dagstuhl Seminar 18431)}},
  pages =	{104--126},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2019},
  volume =	{8},
  number =	{10},
  editor =	{Bickel, Bernd and Alexa, Marc and Hodgins, Jessica K. and Shea, Kristina},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.8.10.104},
  URN =		{urn:nbn:de:0030-drops-103502},
  doi =		{10.4230/DagRep.8.10.104},
  annote =	{Keywords: Computational	Fabrication, Computational Design, Engineering Design, 3D Printing, 4D Printing}
}
Document
DOI: 10.4230/DagRep.8.10.127
Data Physicalization (Dagstuhl Seminar 18441)

Authors: Jason Alexander, Petra Isenberg, Yvonne Jansen, Bernice E. Rogowitz, and Andrew Vande Moere

Abstract
Data physicalization involves representing numbers and relationships using physical, tangible displays. These displays provide tactile, as well as visual metaphors for expressing and experiencing data, and can unlock new analytical insights and emotional responses. This Dagstuhl seminar brought together a diverse group of researchers and practitioners to explore the benefits and challenges of physicalization - computer scientists trained in visualization, virtual reality and human-computer interaction; architects of virtual and augmented systems; perceptual and cognitive scientists; and artists and designers. Through interactive discussions and demonstrations, we explored physicalization, as a set of methodologies for representing data, for engaging audiences, and for artistic expression.
Cite as

Jason Alexander, Petra Isenberg, Yvonne Jansen, Bernice E. Rogowitz, and Andrew Vande Moere. Data Physicalization (Dagstuhl Seminar 18441). In Dagstuhl Reports, Volume 8, Issue 10, pp. 127-147, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@Article{alexander_et_al:DagRep.8.10.127,
  author =	{Alexander, Jason and Isenberg, Petra and Jansen, Yvonne and Rogowitz, Bernice E. and Vande Moere, Andrew},
  title =	{{Data Physicalization (Dagstuhl Seminar 18441)}},
  pages =	{127--147},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2019},
  volume =	{8},
  number =	{10},
  editor =	{Alexander, Jason and Isenberg, Petra and Jansen, Yvonne and Rogowitz, Bernice E. and Vande Moere, Andrew},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.8.10.127},
  URN =		{urn:nbn:de:0030-drops-103513},
  doi =		{10.4230/DagRep.8.10.127},
  annote =	{Keywords: Dagstuhl Seminar, Data Physicalization}
}
Document
DOI: 10.4230/DagRep.8.10.148
Visualization and Processing of Anisotropy in Imaging, Geometry, and Astronomy (Dagstuhl Seminar 18442)

Authors: Andrea Fuster, Evren Özarslan, Thomas Schultz, and Eugene Zhang

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 18442, "Visualization and Processing of Anisotropy in Imaging, Geometry, and Astronomy", which was attended by 30 international researchers, both junior and senior. Directional preferences or anisotropies are encountered across many different disciplines and spatial scales. These disciplines share a need for modeling, processing, and visualizing anisotropic quantities, which poses interesting challenges to applied computer science. With the goal of identifying open problems, making practitioners aware of existing solutions, and discovering synergies between different applications in which anisotropy arises, this seminar brought together researchers working on different aspects of computer science with experts from neuroimaging and astronomy. This report gathers abstracts of the talks held by the participants, as well as an account of topics raised within the breakout sessions.
Cite as

Andrea Fuster, Evren Özarslan, Thomas Schultz, and Eugene Zhang. Visualization and Processing of Anisotropy in Imaging, Geometry, and Astronomy (Dagstuhl Seminar 18442). In Dagstuhl Reports, Volume 8, Issue 10, pp. 148-172, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@Article{fuster_et_al:DagRep.8.10.148,
  author =	{Fuster, Andrea and \"{O}zarslan, Evren and Schultz, Thomas and Zhang, Eugene},
  title =	{{Visualization and Processing of Anisotropy in Imaging, Geometry, and Astronomy (Dagstuhl Seminar 18442)}},
  pages =	{148--172},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2019},
  volume =	{8},
  number =	{10},
  editor =	{Fuster, Andrea and \"{O}zarslan, Evren and Schultz, Thomas and Zhang, Eugene},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.8.10.148},
  URN =		{urn:nbn:de:0030-drops-103524},
  doi =		{10.4230/DagRep.8.10.148},
  annote =	{Keywords: Anisotropy, astronomy, diffusion-weighted imaging (DWI), geometry processing, tensor fields, topology, visualization, uncertainty, shape modeling, microstructure imaging, statistical analysis}
}

Filters

  • Authors
  • A
  • Alexa, Marc
  • Alexander, Jason
  • B
  • Bajpai, Vaibhav
  • Bickel, Bernd
  • Bonaventure, Olivier
  • Breuß, Michael
  • Bruckstein, Alfred M.
  • C
  • Claffy, Kimberly
  • F
  • Fekete, Jean-Daniel
  • Fernau, Henning
  • Fisher, Danyel
  • Fuster, Andrea
  • G
  • Golovach, Petr. A.
  • H
  • Hodgins, Jessica K.
  • I
  • Isenberg, Petra
  • J
  • Jansen, Yvonne
  • K
  • Karrenberg, Daniel
  • Kiselman, Christer Oscar
  • M
  • Maragos, Petros
  • N
  • Nandi, Arnab
  • O
  • Özarslan, Evren
  • R
  • Rogowitz, Bernice E.
  • S
  • Sagot, Marie-France
  • Schultz, Thomas
  • Sedlmair, Michael
  • Shea, Kristina
  • V
  • Vande Moere, Andrew
  • Z
  • Zhang, Eugene

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