Issue
Dagstuhl Reports, Volume 8, Issue 10
-
Part of:
Volume:
Dagstuhl Reports, Volume 8
Journal: Dagstuhl Reports (DagRep)
Publication Details
- published at: 2019-06-18
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- DBLP: db/journals/dagstuhl-reports/dagstuhl-reports8
Access Numbers
- Detailed Access Statistics available here
-
Total Document Accesses (updated on a weekly basis):
0PDF Downloads
Documents
No documents found matching your filter selection.
Document
Complete Issue
Dagstuhl Reports, Volume 8, Issue 10, October 2018, Complete Issue
Abstract
Dagstuhl Reports, Volume 8, Issue 10, October 2018, Complete Issue
Cite as
Dagstuhl Reports, Volume 8, Issue 10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
Copy BibTex To Clipboard
@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-dev.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
Dagstuhl Reports, Table of Contents, Volume 8, Issue 10, 2018
Abstract
Dagstuhl Reports, Table of Contents, Volume 8, Issue 10, 2018
Cite as
Dagstuhl Reports, Volume 8, Issue 10, pp. i-ii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
Copy BibTex To Clipboard
@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-dev.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
Progressive Data Analysis and Visualization (Dagstuhl Seminar 18411)
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)
Copy BibTex To Clipboard
@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-dev.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
Encouraging Reproducibility in Scientific Research of the Internet (Dagstuhl Seminar 18412)
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)
Copy BibTex To Clipboard
@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-dev.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
Algorithmic Enumeration: Output-sensitive, Input-Sensitive, Parameterized, Approximative (Dagstuhl Seminar 18421)
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)
Copy BibTex To Clipboard
@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-dev.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
Shape Analysis: Euclidean, Discrete and Algebraic Geometric Methods (Dagstuhl Seminar 18422)
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)
Copy BibTex To Clipboard
@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-dev.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
Computational Aspects of Fabrication (Dagstuhl Seminar 18431)
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)
Copy BibTex To Clipboard
@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-dev.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
Data Physicalization (Dagstuhl Seminar 18441)
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)
Copy BibTex To Clipboard
@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-dev.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
Visualization and Processing of Anisotropy in Imaging, Geometry, and Astronomy (Dagstuhl Seminar 18442)
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)
Copy BibTex To Clipboard
@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-dev.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}
}