Search Results

Documents authored by Ramon, Jan

Document
DOI: 10.4230/LIPIcs.ICDT.2022.5
Linear Programs with Conjunctive Queries

Authors: Florent Capelli, Nicolas Crosetti, Joachim Niehren, and Jan Ramon

Published in: LIPIcs, Volume 220, 25th International Conference on Database Theory (ICDT 2022)

Abstract
In this paper, we study the problem of optimizing a linear program whose variables are the answers to a conjunctive query. For this we propose the language LP(CQ) for specifying linear programs whose constraints and objective functions depend on the answer sets of conjunctive queries. We contribute an efficient algorithm for solving programs in a fragment of LP(CQ). The naive approach constructs a linear program having as many variables as there are elements in the answer set of the queries. Our approach constructs a linear program having the same optimal value but fewer variables. This is done by exploiting the structure of the conjunctive queries using generalized hypertree decompositions of small width to factorize elements of the answer set together. We illustrate the various applications of LP(CQ) programs on three examples: optimizing deliveries of resources, minimizing noise for differential privacy, and computing the s-measure of patterns in graphs as needed for data mining.
Cite as

Florent Capelli, Nicolas Crosetti, Joachim Niehren, and Jan Ramon. Linear Programs with Conjunctive Queries. In 25th International Conference on Database Theory (ICDT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 220, pp. 5:1-5:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

Copy BibTex To Clipboard

@InProceedings{capelli_et_al:LIPIcs.ICDT.2022.5,
  author =	{Capelli, Florent and Crosetti, Nicolas and Niehren, Joachim and Ramon, Jan},
  title =	{{Linear Programs with Conjunctive Queries}},
  booktitle =	{25th International Conference on Database Theory (ICDT 2022)},
  pages =	{5:1--5:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-223-5},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{220},
  editor =	{Olteanu, Dan and Vortmeier, Nils},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2022.5},
  URN =		{urn:nbn:de:0030-drops-158796},
  doi =		{10.4230/LIPIcs.ICDT.2022.5},
  annote =	{Keywords: Database queries, linear programming, hypergraph decomposition}
}
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 Imprint Privacy Contact