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Dagstuhl Reports, Volume 10, Issue 2
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Dagstuhl Reports, Volume 10
Journal: Dagstuhl Reports (DagRep)
Publication Details
- published at: 2020-09-17
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- DBLP: db/journals/dagstuhl-reports/dagstuhl-reports10
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Complete Issue
Dagstuhl Reports, Volume 10, Issue 2, February 2020, Complete Issue
Abstract
Dagstuhl Reports, Volume 10, Issue 2, February 2020, Complete Issue
Cite as
Dagstuhl Reports, Volume 10, Issue 2, pp. 1-89, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@Article{DagRep.10.2,
title = {{Dagstuhl Reports, Volume 10, Issue 2, February 2020, Complete Issue}},
pages = {1--89},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2020},
volume = {10},
number = {2},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.10.2},
URN = {urn:nbn:de:0030-drops-130554},
doi = {10.4230/DagRep.10.2},
annote = {Keywords: Dagstuhl Reports, Volume 10, Issue 2, February 2020, Complete Issue}
}
Document
Front Matter
Dagstuhl Reports, Table of Contents, Volume 10, Issue 2, 2020
Abstract
Dagstuhl Reports, Table of Contents, Volume 10, Issue 2, 2020
Cite as
Dagstuhl Reports, Volume 10, Issue 2, pp. i-ii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@Article{DagRep.10.2.i,
title = {{Dagstuhl Reports, Table of Contents, Volume 10, Issue 2, 2020}},
pages = {i--ii},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2020},
volume = {10},
number = {2},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.10.2.i},
URN = {urn:nbn:de:0030-drops-130569},
doi = {10.4230/DagRep.10.2.i},
annote = {Keywords: Table of Contents, Frontmatter}
}
Document
SAT and Interactions (Dagstuhl Seminar 20061)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 20061 "SAT and Interactions". The seminar brought together theoreticians and practitioners from the areas of proof complexity and proof theory, SAT and QBF solving, MaxSAT, and modal logics, who discussed recent developments in their fields and embarked on an interdisciplinary exchange of ideas and techniques between these neighbouring subfields of SAT.
Cite as
Olaf Beyersdorff, Uwe Egly, Meena Mahajan, and Cláudia Nalon. SAT and Interactions (Dagstuhl Seminar 20061). In Dagstuhl Reports, Volume 10, Issue 2, pp. 1-18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@Article{beyersdorff_et_al:DagRep.10.2.1,
author = {Beyersdorff, Olaf and Egly, Uwe and Mahajan, Meena and Nalon, Cl\'{a}udia},
title = {{SAT and Interactions (Dagstuhl Seminar 20061)}},
pages = {1--18},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2020},
volume = {10},
number = {2},
editor = {Beyersdorff, Olaf and Egly, Uwe and Mahajan, Meena and Nalon, Cl\'{a}udia},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.10.2.1},
URN = {urn:nbn:de:0030-drops-130576},
doi = {10.4230/DagRep.10.2.1},
annote = {Keywords: SAT, MaxSAT, QBF, proof complexity, deep inference, modal logic, solving}
}
Document
Foundations of Composite Event Recognition (Dagstuhl Seminar 20071)
Abstract
Composite Event Recognition (CER) refers to the activity of detecting patterns in streams of continuously arriving "event" data over, possibly geographically, distributed sources. CER is key in Big Data applications that require the processing of such event streams to obtain timely insights and to implement reactive and proactive measures. Examples include the recognition of emerging stories and trends on the Social Web, traffic and transport incidents in smart cities, and epidemic spread.
Numerous CER languages have been proposed in the literature. While these systems have a common goal, they differ in their data models, pattern languages and processing mechanisms, resulting in heterogeneous implementations with fundamentally different capabilities. Moreover, we lack a common understanding of the trade-offs between expressiveness and complexity, and a theory for comparing the fundamental capabilities of CER systems. As such, CER frameworks are difficult to understand, extend and generalise. It is unclear which of the proposed approaches better meets the requirements of a given application. Furthermore, the lack of foundations makes it hard to leverage established results - from automata theory, temporal logics, etc - thus hindering scientific and technological progress in CER.
The objective of the seminar was to bring together researchers and practitioners working in Databases, Distributed Systems, Automata Theory, Logic and Stream Reasoning; disseminate the recent foundational results across these fields; establish new research collaborations among these fields; thereby start making progress towards formulating such foundations.
Cite as
Alexander Artikis, Thomas Eiter, Alessandro Margara, and Stijn Vansummeren. Foundations of Composite Event Recognition (Dagstuhl Seminar 20071). In Dagstuhl Reports, Volume 10, Issue 2, pp. 19-49, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@Article{artikis_et_al:DagRep.10.2.19,
author = {Artikis, Alexander and Eiter, Thomas and Margara, Alessandro and Vansummeren, Stijn},
title = {{Foundations of Composite Event Recognition (Dagstuhl Seminar 20071)}},
pages = {19--49},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2020},
volume = {10},
number = {2},
editor = {Artikis, Alexander and Eiter, Thomas and Margara, Alessandro and Vansummeren, Stijn},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.10.2.19},
URN = {urn:nbn:de:0030-drops-130587},
doi = {10.4230/DagRep.10.2.19},
annote = {Keywords: complex event processing, event algebra, pattern matching, stream reasoning, temporal reasoning}
}
Document
Scheduling (Dagstuhl Seminar 20081)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 20081 "Scheduling". The seminar focused on the interplay between scheduling problems and problems that arise in the management of transportation and traffic. Important aspects at the intersection of these two research directions include data-driven approaches in dynamic decision-making, scheduling in combination with routing, shared mobility, and coordination versus competition.
Cite as
Nicole Megow, David Shmoys, and Ola Svensson. Scheduling (Dagstuhl Seminar 20081). In Dagstuhl Reports, Volume 10, Issue 2, pp. 50-75, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@Article{megow_et_al:DagRep.10.2.50,
author = {Megow, Nicole and Shmoys, David and Svensson, Ola},
title = {{Scheduling (Dagstuhl Seminar 20081)}},
pages = {50--75},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2020},
volume = {10},
number = {2},
editor = {Megow, Nicole and Shmoys, David and Svensson, Ola},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.10.2.50},
URN = {urn:nbn:de:0030-drops-130590},
doi = {10.4230/DagRep.10.2.50},
annote = {Keywords: scheduling, optimization, approximation algorithms, routing, transportation, mechanism design}
}
Document
SE4ML - Software Engineering for AI-ML-based Systems (Dagstuhl Seminar 20091)
Abstract
Multiple research disciplines, from cognitive sciences to biology, finance, physics, and the social sciences, as well as many companies, believe that data-driven and intelligent solutions are necessary. Unfortunately, current artificial intelligence (AI) and machine learning (ML) technologies are not sufficiently democratized - building complex AI and ML systems requires deep expertise in computer science and extensive programming skills to work with various machine reasoning and learning techniques at a rather low level of abstraction. It also requires extensive trial and error exploration for model selection, data cleaning, feature selection, and parameter tuning. Moreover, there is a lack of theoretical understanding that could be used to abstract away these subtleties. Conventional programming languages and software engineering paradigms have also not been designed to address challenges faced by AI and ML practitioners. In 2016, companies invested $26–39 billion in AI and McKinsey predicts that investments will be growing over the next few years. Any AI/ML-based systems will need to be built, tested, and maintained, yet there is a lack of established engineering practices in industry for such systems because they are fundamentally different from traditional software systems.
This Dagstuhl Seminar brought together two rather disjoint communities together, software engineering and programming languages (PL/SE) and artificial intelligence and machine learning (AI-ML) to discuss open problems on how to improve the productivity of data scientists, software engineers, and AI-ML practitioners in industry.
Cite as
Kristian Kersting, Miryung Kim, Guy Van den Broeck, and Thomas Zimmermann. SE4ML - Software Engineering for AI-ML-based Systems (Dagstuhl Seminar 20091). In Dagstuhl Reports, Volume 10, Issue 2, pp. 76-87, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@Article{kersting_et_al:DagRep.10.2.76,
author = {Kersting, Kristian and Kim, Miryung and Van den Broeck, Guy and Zimmermann, Thomas},
title = {{SE4ML - Software Engineering for AI-ML-based Systems (Dagstuhl Seminar 20091)}},
pages = {76--87},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2020},
volume = {10},
number = {2},
editor = {Kersting, Kristian and Kim, Miryung and Van den Broeck, Guy and Zimmermann, Thomas},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.10.2.76},
URN = {urn:nbn:de:0030-drops-130603},
doi = {10.4230/DagRep.10.2.76},
annote = {Keywords: correctness / explainability / traceability / fairness for ml, data scientist productivity, debugging/ testing / verification for ml systems}
}