6 Search Results for "Winter, Stefan"

Issue

DARTS, Volume 9, Issue 2

Special Issue of the 37th European Conference on Object-Oriented Programming (ECOOP 2023)

Editors: Hernán Ponce de León and Stefan Winter

Issue

DARTS, Volume 8, Issue 2

Special Issue of the 36th European Conference on Object-Oriented Programming (ECOOP 2022)

Editors: Alessandra Gorla and Stefan Winter

Document
Front Matter
DOI: 10.4230/DARTS.9.2.0
Front Matter - ECOOP 2023 Artifacts, Table of Contents, Preface, Artifact Evaluation Committee

Authors: Hernán Ponce de León and Stefan Winter

Published in: DARTS, Volume 9, Issue 2, Special Issue of the 37th European Conference on Object-Oriented Programming (ECOOP 2023)

Abstract
Front Matter - ECOOP 2023 Artifacts, Table of Contents, Preface, Artifact Evaluation Committee
Cite as

Special Issue of the 37th European Conference on Object-Oriented Programming (ECOOP 2023). Dagstuhl Artifacts Series (DARTS), Volume 9, Issue 2, pp. 0:i-0:xii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{deleon_et_al:DARTS.9.2.0,
  author =	{de Le\'{o}n, Hern\'{a}n Ponce and Winter, Stefan},
  title =	{{Front Matter - ECOOP 2023 Artifacts, Table of Contents, Preface, Artifact Evaluation Committee}},
  pages =	{0:i--0:xii},
  journal =	{Dagstuhl Artifacts Series},
  ISSN =	{2509-8195},
  year =	{2023},
  volume =	{9},
  number =	{2},
  editor =	{de Le\'{o}n, Hern\'{a}n Ponce and Winter, Stefan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DARTS.9.2.0},
  URN =		{urn:nbn:de:0030-drops-182401},
  doi =		{10.4230/DARTS.9.2.0},
  annote =	{Keywords: Front Matter - ECOOP 2023 Artifacts, Table of Contents, Preface, Artifact Evaluation Committee}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.COSIT.2022.25
Collaborative Wayfinding Under Distributed Spatial Knowledge (Short Paper)

Authors: Panagiotis Mavros, Saskia Kuliga, Ed Manley, Hilal Rohaidi Fitri, Michael Joos, and Christoph Hölscher

Published in: LIPIcs, Volume 240, 15th International Conference on Spatial Information Theory (COSIT 2022)

Abstract
In many everyday situations, two or more people navigate collaboratively but their spatial knowledge does not necessarily overlap. However, most research to date, has investigated social wayfinding under either 1-sided or fully shared spatial information. Here, we present the pilot experiment of a novel, computerised, non-verbal experimental paradigm to study collaborative wayfinding under the face of spatial information uncertainty. Participants (N=32) learned two different neighbourhoods individually, and then navigated together as dyads (D=16), from one neighbourhood to the other. Our pilot results reveal that overall participants share navigational control, but are in control more when the task leads them to a familiar destination. We discuss the effects of spatial ability and motivation to lead, as well as the outlook of the paradigm.
Cite as

Panagiotis Mavros, Saskia Kuliga, Ed Manley, Hilal Rohaidi Fitri, Michael Joos, and Christoph Hölscher. Collaborative Wayfinding Under Distributed Spatial Knowledge (Short Paper). In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 25:1-25:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{mavros_et_al:LIPIcs.COSIT.2022.25,
  author =	{Mavros, Panagiotis and Kuliga, Saskia and Manley, Ed and Fitri, Hilal Rohaidi and Joos, Michael and H\"{o}lscher, Christoph},
  title =	{{Collaborative Wayfinding Under Distributed Spatial Knowledge}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{25:1--25:10},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-257-0},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{240},
  editor =	{Ishikawa, Toru and Fabrikant, Sara Irina and Winter, Stephan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2022.25},
  URN =		{urn:nbn:de:0030-drops-169105},
  doi =		{10.4230/LIPIcs.COSIT.2022.25},
  annote =	{Keywords: navigation, wayfinding, collaboration, dyad, online}
}
Document
Front Matter
DOI: 10.4230/DARTS.8.2.0
Front Matter - ECOOP 2022 Artifacts, Table of Contents, Preface, Artifact Evaluation Committee

Authors: Alessandra Gorla and Stefan Winter

Published in: DARTS, Volume 8, Issue 2, Special Issue of the 36th European Conference on Object-Oriented Programming (ECOOP 2022)

Abstract
Front Matter - ECOOP 2022 Artifacts, Table of Contents, Preface, Artifact Evaluation Committee
Cite as

Special Issue of the 36th European Conference on Object-Oriented Programming (ECOOP 2022). Dagstuhl Artifacts Series (DARTS), Volume 8, Issue 2, pp. 0:i-0:xii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@Article{gorla_et_al:DARTS.8.2.0,
  author =	{Gorla, Alessandra and Winter, Stefan},
  title =	{{Front Matter - ECOOP 2022 Artifacts, Table of Contents, Preface, Artifact Evaluation Committee}},
  pages =	{0:i--0:xii},
  journal =	{Dagstuhl Artifacts Series},
  ISSN =	{2509-8195},
  year =	{2022},
  volume =	{8},
  number =	{2},
  editor =	{Gorla, Alessandra and Winter, Stefan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DARTS.8.2.0},
  URN =		{urn:nbn:de:0030-drops-161982},
  doi =		{10.4230/DARTS.8.2.0},
  annote =	{Keywords: Front Matter - ECOOP 2022 Artifacts, Table of Contents, Preface, Artifact Evaluation Committee}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GISCIENCE.2018.60
Abstract Data Types for Spatio-Temporal Remote Sensing Analysis (Short Paper)

Authors: Martin Sudmanns, Stefan Lang, Dirk Tiede, Christian Werner, Hannah Augustin, and Andrea Baraldi

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
Abstract data types are a helpful framework to formalise analyses and make them more transparent, reproducible and comprehensible. We are revisiting an approach based on the space, time and theme dimensions of remotely sensed data, and extending it with a more differentiated understanding of space-time representations. In contrast to existing approaches and implementations that consider only fixed spatial units (e.g. pixels), our approach allows investigations of the spatial units' spatio-temporal characteristics, such as the size and shape of their geometry, and their relationships. Five different abstract data types are identified to describe geographical phenomenon, either directly or in combination: coverage, time series, trajectory, composition and evolution.
Cite as

Martin Sudmanns, Stefan Lang, Dirk Tiede, Christian Werner, Hannah Augustin, and Andrea Baraldi. Abstract Data Types for Spatio-Temporal Remote Sensing Analysis (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 60:1-60:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{sudmanns_et_al:LIPIcs.GISCIENCE.2018.60,
  author =	{Sudmanns, Martin and Lang, Stefan and Tiede, Dirk and Werner, Christian and Augustin, Hannah and Baraldi, Andrea},
  title =	{{Abstract Data Types for Spatio-Temporal Remote Sensing Analysis}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{60:1--60:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.60},
  URN =		{urn:nbn:de:0030-drops-93881},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.60},
  annote =	{Keywords: Big Earth Data, Semantic Analysis, Data Cube}
}
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