Search Results

Documents authored by Verwer, Sicco

Document
DOI: 10.4230/OASIcs.Commit2Data.5
Real-Time Data-Driven Maintenance Logistics: A Public-Private Collaboration

Authors: Willem van Jaarsveld, Alp Akçay, Laurens Bliek, Paulo da Costa, Mathijs de Weerdt, Rik Eshuis, Stella Kapodistria, Uzay Kaymak, Verus Pronk, Geert-Jan van Houtum, Peter Verleijsdonk, Sicco Verwer, Simon Voorberg, and Yingqian Zhang

Published in: OASIcs, Volume 124, Commit2Data (2024)

Abstract
The project "Real-time data-driven maintenance logistics" was initiated with the purpose of bringing innovations in data-driven decision making to maintenance logistics, by bringing problem owners in the form of three innovative companies together with researchers at two leading knowledge institutions. This paper reviews innovations in three related areas: How the innovations were inspired by practice, how they materialized, and how the results impact practice.
Cite as

Willem van Jaarsveld, Alp Akçay, Laurens Bliek, Paulo da Costa, Mathijs de Weerdt, Rik Eshuis, Stella Kapodistria, Uzay Kaymak, Verus Pronk, Geert-Jan van Houtum, Peter Verleijsdonk, Sicco Verwer, Simon Voorberg, and Yingqian Zhang. Real-Time Data-Driven Maintenance Logistics: A Public-Private Collaboration. In Commit2Data. Open Access Series in Informatics (OASIcs), Volume 124, pp. 5:1-5:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

Copy BibTex To Clipboard

@InProceedings{vanjaarsveld_et_al:OASIcs.Commit2Data.5,
  author =	{van Jaarsveld, Willem and Ak\c{c}ay, Alp and Bliek, Laurens and da Costa, Paulo and de Weerdt, Mathijs and Eshuis, Rik and Kapodistria, Stella and Kaymak, Uzay and Pronk, Verus and van Houtum, Geert-Jan and Verleijsdonk, Peter and Verwer, Sicco and Voorberg, Simon and Zhang, Yingqian},
  title =	{{Real-Time Data-Driven Maintenance Logistics: A Public-Private Collaboration}},
  booktitle =	{Commit2Data},
  pages =	{5:1--5:13},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-351-5},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{124},
  editor =	{Haverkort, Boudewijn R. and de Jongste, Aldert and van Kuilenburg, Pieter and Vromans, Ruben D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Commit2Data.5},
  URN =		{urn:nbn:de:0030-drops-213626},
  doi =		{10.4230/OASIcs.Commit2Data.5},
  annote =	{Keywords: Data, Maintenance, Logistics, Optimization, Research, Project}
}
Document
DOI: 10.4230/LIPIcs.ICLP.2012.14
Modeling Machine Learning and Data Mining Problems with FO(·)

Authors: Hendrik Blockeel, Bart Bogaerts, Maurice Bruynooghe, Broes De Cat, Stef De Pooter, Marc Denecker, Anthony Labarre, Jan Ramon, and Sicco Verwer

Published in: LIPIcs, Volume 17, Technical Communications of the 28th International Conference on Logic Programming (ICLP'12) (2012)

Abstract
This paper reports on the use of the FO(·) language and the IDP framework for modeling and solving some machine learning and data mining tasks. The core component of a model in the IDP framework is an FO(·) theory consisting of formulas in first order logic and definitions; the latter are basically logic programs where clause bodies can have arbitrary first order formulas. Hence, it is a small step for a well-versed computer scientist to start modeling. We describe some models resulting from the collaboration between IDP experts and domain experts solving machine learning and data mining tasks. A first task is in the domain of stemmatology, a domain of philology concerned with the relationship between surviving variant versions of text. A second task is about a somewhat similar problem within biology where phylogenetic trees are used to represent the evolution of species. A third and final task is about learning a minimal automaton consistent with a given set of strings. For each task, we introduce the problem, present the IDP code and report on some experiments.
Cite as

Hendrik Blockeel, Bart Bogaerts, Maurice Bruynooghe, Broes De Cat, Stef De Pooter, Marc Denecker, Anthony Labarre, Jan Ramon, and Sicco Verwer. Modeling Machine Learning and Data Mining Problems with FO(·). In Technical Communications of the 28th International Conference on Logic Programming (ICLP'12). Leibniz International Proceedings in Informatics (LIPIcs), Volume 17, pp. 14-25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

Copy BibTex To Clipboard

@InProceedings{blockeel_et_al:LIPIcs.ICLP.2012.14,
  author =	{Blockeel, Hendrik and Bogaerts, Bart and Bruynooghe, Maurice and De Cat, Broes and De Pooter, Stef and Denecker, Marc and Labarre, Anthony and Ramon, Jan and Verwer, Sicco},
  title =	{{Modeling Machine Learning and Data Mining Problems with FO(·)}},
  booktitle =	{Technical Communications of the 28th International Conference on Logic Programming (ICLP'12)},
  pages =	{14--25},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-43-9},
  ISSN =	{1868-8969},
  year =	{2012},
  volume =	{17},
  editor =	{Dovier, Agostino and Santos Costa, V{\'\i}tor},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICLP.2012.14},
  URN =		{urn:nbn:de:0030-drops-36049},
  doi =		{10.4230/LIPIcs.ICLP.2012.14},
  annote =	{Keywords: Knowledge representation and reasoning, declarative modeling, logic programming, knowledge base systems, FO(·), IDP framework, stemmatology, phylogene}
}
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact