Issue
Dagstuhl Reports, Volume 15, Issue 4
-
Part of:
Volume:
Dagstuhl Reports, Volume 15
Journal: Dagstuhl Reports (DagRep)
Event
- Dagstuhl Seminars 25151, 25152, 25171, 25172, 25181, 25182
Publication Details
- published at: 2025-12-15
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
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Document
Complete Issue
Dagstuhl Reports, Volume 15, Issue 4, April 2025, Complete Issue
Abstract
Dagstuhl Reports, Volume 15, Issue 4, April 2025, Complete Issue
Cite as
Dagstuhl Reports, Volume 15, Issue 4, pp. 1-140, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{DagRep.15.4,
title = {{Dagstuhl Reports, Volume 15, Issue 4, April 2025, Complete Issue}},
pages = {1--140},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {15},
number = {4},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.4},
URN = {urn:nbn:de:0030-drops-252711},
doi = {10.4230/DagRep.15.4},
annote = {Keywords: Dagstuhl Reports, Volume 15, Issue 4, April 2025, Complete Issue}
}
Document
Front Matter
Dagstuhl Reports, Table of Contents, Volume 15, Issue 4, 2025
Abstract
Dagstuhl Reports, Table of Contents, Volume 15, Issue 4, 2025
Cite as
Dagstuhl Reports, Volume 15, Issue 4, pp. i-ii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{DagRep.15.4.i,
title = {{Dagstuhl Reports, Table of Contents, Volume 15, Issue 4, 2025}},
pages = {i--ii},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {15},
number = {4},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.4.i},
URN = {urn:nbn:de:0030-drops-238285},
doi = {10.4230/DagRep.15.4.i},
annote = {Keywords: Table of Contents, Frontmatter}
}
Document
Disruptive Memory Technologies (Dagstuhl Seminar 25151)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 25151 "Disruptive Memory Technologies".
Memory is a central component in every computer system. Hardware evolution has lead to greater capacities and higher speeds, but essential properties of its hardware/software interface have been unchanged for decades: Main memories used to be passive, largely homogeneous, and volatile. These properties are now so firmly anchored in the expectations of software developers that they manifest in their products.
However, a wave of innovations is currently shattering these assumptions. In this sense, several new memory technologies are disruptive for the entire software industry. For example, new servers combine "high-bandwidth memory" with classic memory modules and "CXL" enables even more hybrid architectures (non-homogeneous). The "in-/near-memory" computing approaches abandon the Von Neumann architecture and promise huge performance improvements by allowing CPU-independent processing of data objects in or close to the memory (non-passive). Finally, "persistent memory" is available for servers and embedded systems (non-volatile).
Overall, the expectations are high. Computers could have lower energy consumption, more performance, improved reliability, and reduced costs. However, from the (system) software perspective it is largely unclear how to use the novel memory technology efficiently. The seminar tackled this problem by discussing the state and potential of disruptive memory technologies, the challenges for system and application software, and important research directions.
Cite as
Haibo Chen, Ada Gavrilovska, Jana Giceva, and Olaf Spinczyk. Disruptive Memory Technologies (Dagstuhl Seminar 25151). In Dagstuhl Reports, Volume 15, Issue 4, pp. 1-27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{chen_et_al:DagRep.15.4.1,
author = {Chen, Haibo and Gavrilovska, Ada and Giceva, Jana and Spinczyk, Olaf},
title = {{Disruptive Memory Technologies (Dagstuhl Seminar 25151)}},
pages = {1--27},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {15},
number = {4},
editor = {Chen, Haibo and Gavrilovska, Ada and Giceva, Jana and Spinczyk, Olaf},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.4.1},
URN = {urn:nbn:de:0030-drops-252581},
doi = {10.4230/DagRep.15.4.1},
annote = {Keywords: data-centric computing, disaggregated memory, persistent memory (pmem), processing in memory (pim), system software stack}
}
Document
Multi-Faceted Visual Process Mining and Analytics (Dagstuhl Seminar 25152)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 25152 "Multi-Faceted Visual Process Mining and Analytics". The seminar brought together experts from the process mining (PM) community and the visual analytics (VA) community to strengthen the identified synergies of both fields and identify further novel and promising research directions. A particular focus of the seminar was on the challenges arising from the multi-faceted nature of processes and the multi-faceted data to be investigated. The relevant facets include time (when do processes happen), space (where do processes happen), topology (how are processes connected), object centricity (how are processes characterized), uncertainty (what are we unsure about), analytic provenance (how did we obtain our knowledge), and more. This report deals with challenges related to these different data facets, individually and in combination. As a general principle, VA methods are advocated to be an integral part of all phases of the PM process to facilitate a comprehensive multi-faceted data exploration, hypothesis generation, and presentation of results. More concretely, the discussions revolve around several aspects at the crossroads of the two disciplines workflows, including the data facets under analysis, the human factors at play, the catalog of aided tasks, novel combinations of visual, interactive, and computational methods, as well as integration, scalability, and general applicability of the devised solutions.
Cite as
Claudio Di Ciccio, Pnina Soffer, Christian Tominski, Katerina Vrotsou, and Giovanni Meroni. Multi-Faceted Visual Process Mining and Analytics (Dagstuhl Seminar 25152). In Dagstuhl Reports, Volume 15, Issue 4, pp. 28-78, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{diciccio_et_al:DagRep.15.4.28,
author = {Di Ciccio, Claudio and Soffer, Pnina and Tominski, Christian and Vrotsou, Katerina and Meroni, Giovanni},
title = {{Multi-Faceted Visual Process Mining and Analytics (Dagstuhl Seminar 25152)}},
pages = {28--78},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {15},
number = {4},
editor = {Di Ciccio, Claudio and Soffer, Pnina and Tominski, Christian and Vrotsou, Katerina and Meroni, Giovanni},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.4.28},
URN = {urn:nbn:de:0030-drops-252573},
doi = {10.4230/DagRep.15.4.28},
annote = {Keywords: human in the loop, process mining, visual analytics}
}
Document
Holistic Graph-Processing Systems: Enabling Real-World Scale and Societal Impact (Dagstuhl Seminar 25171)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 25171, "Holistic Graph-Processing Systems: Enabling Real-World Scale and Societal Impact".
Motivated by the need to tackle the challenges that massive and complex data production and consumption bring to our interconnected, digital world, this seminar focused on large-scale graph processing as a systematic approach to transform these challenges into opportunities. Graphs provide a universal mathematical abstraction for such data, and they already influence various sectors - such as logistics, drug discovery, or fraud detection. However, we have only begun to realize their potential. Nevertheless, the benefits of graph processing could be canceled out by the rapid increase in data scale and diversity, as well as the increasing complexity in developing, executing, and sharing graph-based algorithms and workflows. The emerging field of graph processing systems promises to tackle these challenges. To make such systems effective and efficient, and facilitate their adoption, we need holistic approaches to cope with data transformation and ingestion, workload and system dynamics, high-tier graph programming and co-design with the platform, the emerging computing continuum, and domain-specific needs, among others.
Our seminar explored the symbiosis of graph systems, machine learning, and network science by bringing together researchers, developers, and practitioners actively working on these topics with a focus on graphs. The seminar featured a mix of invited talks, expert panels, and focused discussion groups. The report documents these different elements, summarizes the main findings, and identifies the open problems and challenges that we will tackle next as a joint community.
Cite as
Alexandru Iosup, Ana Lucia Varbanescu, Hannes Voigt, and Jože Rožanec. Holistic Graph-Processing Systems: Enabling Real-World Scale and Societal Impact (Dagstuhl Seminar 25171). In Dagstuhl Reports, Volume 15, Issue 4, pp. 79-91, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{iosup_et_al:DagRep.15.4.79,
author = {Iosup, Alexandru and Varbanescu, Ana Lucia and Voigt, Hannes and Ro\v{z}anec, Jo\v{z}e},
title = {{Holistic Graph-Processing Systems: Enabling Real-World Scale and Societal Impact (Dagstuhl Seminar 25171)}},
pages = {79--91},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {15},
number = {4},
editor = {Iosup, Alexandru and Varbanescu, Ana Lucia and Voigt, Hannes and Ro\v{z}anec, Jo\v{z}e},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.4.79},
URN = {urn:nbn:de:0030-drops-252708},
doi = {10.4230/DagRep.15.4.79},
annote = {Keywords: digital continuum choreography, graph processing optimization, machine learning on graphs, massive graphs, sustainable distributed graph processing}
}
Document
Information Exchange in Software Verification (Dagstuhl Seminar 25172)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 25172 Information Exchange in Software Verification. The term "software verification" refers to the procedure of deciding the correctness of software with respect to (user-supplied or predefined) specifications. In general, software verification is an undecidable problem. Despite this undecidability, software verification is a very active research field with contributions of researchers from several areas such as theorem proving, deductive verification, static analysis, and automatic verification. The analysis techniques developed in these subareas are often complementary with respect to the type of software and specifications they can efficiently handle.
The objective of this Dagstuhl Seminar was to bring together people working in these different subareas to discuss and advance ways of having tools and techniques cooperate on the task of software verification.
Cite as
Dirk Beyer, Marieke Huisman, Jan Strejček, and Heike Wehrheim. Information Exchange in Software Verification (Dagstuhl Seminar 25172). In Dagstuhl Reports, Volume 15, Issue 4, pp. 92-111, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{beyer_et_al:DagRep.15.4.92,
author = {Beyer, Dirk and Huisman, Marieke and Strej\v{c}ek, Jan and Wehrheim, Heike},
title = {{Information Exchange in Software Verification (Dagstuhl Seminar 25172)}},
pages = {92--111},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {15},
number = {4},
editor = {Beyer, Dirk and Huisman, Marieke and Strej\v{c}ek, Jan and Wehrheim, Heike},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.4.92},
URN = {urn:nbn:de:0030-drops-252559},
doi = {10.4230/DagRep.15.4.92},
annote = {Keywords: Competitions and Benchmarks, Data-Flow Analysis, Deductive Verification, Formal Verification, Model Checking}
}
Document
Learned Predictions for Data Structures and Running Time (Dagstuhl Seminar 25181)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 25181 "Learned Predictions for Data Structures and Running Time". The focus of the seminar was applying the new algorithms-with-predictions framework to improve worst-case running time of algorithms and data structures.
This seminar brought together researchers from the data structures, combinatorial optimization and learned predictions communities to address the challenges of adopting learned machine-learned predictions for improving running time guarantees.
Cite as
Inge Li Gørtz, Benjamin J. Moseley, Shikha Singh, and Sergei Vassilvitskii. Learned Predictions for Data Structures and Running Time (Dagstuhl Seminar 25181). In Dagstuhl Reports, Volume 15, Issue 4, pp. 112-125, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{gortz_et_al:DagRep.15.4.112,
author = {G{\o}rtz, Inge Li and Moseley, Benjamin J. and Singh, Shikha and Vassilvitskii, Sergei},
title = {{Learned Predictions for Data Structures and Running Time (Dagstuhl Seminar 25181)}},
pages = {112--125},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {15},
number = {4},
editor = {G{\o}rtz, Inge Li and Moseley, Benjamin J. and Singh, Shikha and Vassilvitskii, Sergei},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.4.112},
URN = {urn:nbn:de:0030-drops-252544},
doi = {10.4230/DagRep.15.4.112},
annote = {Keywords: algorithms with predictions, approximation algorithms, beyond-worst-case analysis, data structures, learning-augmented algorithms}
}
Document
Challenges and Opportunities of Table Representation Learning (Dagstuhl Seminar 25182)
Abstract
The growing volume and importance of structured data have sparked increasing interest in Table Representation Learning (TRL), an emerging field that leverages neural models to learn abstract, general-purpose representations for tabular data to support a wide range of downstream tasks such as tabular prediction, table question answering, tabular data cleaning, and many more. This seminar gathered the different communities (ML, NLP, IR, DB) who work on this topic to discuss the challenges & long-term vision of this field.
From the organizers: Carsten Binnig, Julian Eisenschlos, Madelon Hulsebos, Frank Hutter.
Cite as
Carsten Binnig, Julian Martin Eisenschlos, Madelon Hulsebos, and Frank Hutter. Challenges and Opportunities of Table Representation Learning (Dagstuhl Seminar 25182). In Dagstuhl Reports, Volume 15, Issue 4, pp. 126-138, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
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@Article{binnig_et_al:DagRep.15.4.126,
author = {Binnig, Carsten and Eisenschlos, Julian Martin and Hulsebos, Madelon and Hutter, Frank},
title = {{Challenges and Opportunities of Table Representation Learning (Dagstuhl Seminar 25182)}},
pages = {126--138},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2025},
volume = {15},
number = {4},
editor = {Binnig, Carsten and Eisenschlos, Julian Martin and Hulsebos, Madelon and Hutter, Frank},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.4.126},
URN = {urn:nbn:de:0030-drops-252531},
doi = {10.4230/DagRep.15.4.126},
annote = {Keywords: applications of table representation learning, benchmarks and datasets for table representation learning, pre-trained (language) models for tables and databases, representation and generative learning for data management and analysis}
}