9 Search Results for "Sierra, Alberto"

Document
DOI: 10.4230/OASIcs.DX.2025.6
Towards Predictive Maintenance in an Aluminum Die-Casting Process Using Deep Learning Clustering and Dimensionality Reduction

Authors: Miguel Cubero, Luis Ignacio Jiménez, Daniel López, Belarmino Pulido, and Carlos Alonso-González

Published in: OASIcs, Volume 136, 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)

Abstract
In the manufacturing industry, predictive maintenance requires the estimation of the health status of key subsystems or components. In this study, we will look for degradation patterns in the piston of an injection machine used in an aluminum die casting process operating in an automobile factory in Valladolid (Spain). The injection machine produces a new engine block every 90 seconds and each injection device provides 2000 measurements of various physical variables. This study faced the challenge of finding piston head degradation patterns for an injection machine in the factory, using time series data obtained from the controller, as a preliminary step to estimate the remaining useful life (RUL) of the piston head. The proposed solution used advanced deep learning clustering techniques to generate an index related with the progression of the degradation of the components. The results indicated that degradation patterns can be identified. Later on, using an exponential function an approximation of the RUL can be provided to the plant operator to achieve an ordered piston replacement.
Cite as

Miguel Cubero, Luis Ignacio Jiménez, Daniel López, Belarmino Pulido, and Carlos Alonso-González. Towards Predictive Maintenance in an Aluminum Die-Casting Process Using Deep Learning Clustering and Dimensionality Reduction. In 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025). Open Access Series in Informatics (OASIcs), Volume 136, pp. 6:1-6:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{cubero_et_al:OASIcs.DX.2025.6,
  author =	{Cubero, Miguel and Jim\'{e}nez, Luis Ignacio and L\'{o}pez, Daniel and Pulido, Belarmino and Alonso-Gonz\'{a}lez, Carlos},
  title =	{{Towards Predictive Maintenance in an Aluminum Die-Casting Process Using Deep Learning Clustering and Dimensionality Reduction}},
  booktitle =	{36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)},
  pages =	{6:1--6:16},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-394-2},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{136},
  editor =	{Quinones-Grueiro, Marcos and Biswas, Gautam and Pill, Ingo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2025.6},
  URN =		{urn:nbn:de:0030-drops-247951},
  doi =		{10.4230/OASIcs.DX.2025.6},
  annote =	{Keywords: Prognostics, Deep Learning, Clustering, UMAP, LOWESS regression}
}
Document
DOI: 10.4230/OASIcs.SLATE.2025.5
CVTool: Automating Content Variants of CVs

Authors: Julio Beites Gonçalves, Maria João Varanda Pereira, and Pedro Rangel Henriques

Published in: OASIcs, Volume 135, 14th Symposium on Languages, Applications and Technologies (SLATE 2025)

Abstract
As academic professionals, we frequently need to create different versions of our CVs for project applications, career evaluations, or competitions. These versions may be chronologically structured or skill-oriented, covering specific periods and written in various languages. Even when using a LaTeX document as a base, numerous modifications are required each time an updated CV version is requested for a specific purpose. The primary objective of the project reported in this paper is to design and implement a web-based system (CVTool) that simplifies the management of LaTeX CV content while ensuring flexibility. The CVTool is built on a domain-specific language that enables the creation of various filters, allowing for the automatic content adjustment while preserving the original format. Information is extracted from the LaTeX document, and users can specify the sections, dates, skills they want to highlight, and the language in which the CV should be generated. Since the approach relies on an internal data representation derived from the original LaTeX document, it offers users the flexibility to manage content efficiently and extract the necessary information with ease.
Cite as

Julio Beites Gonçalves, Maria João Varanda Pereira, and Pedro Rangel Henriques. CVTool: Automating Content Variants of CVs. In 14th Symposium on Languages, Applications and Technologies (SLATE 2025). Open Access Series in Informatics (OASIcs), Volume 135, pp. 5:1-5:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{goncalves_et_al:OASIcs.SLATE.2025.5,
  author =	{Gon\c{c}alves, Julio Beites and Pereira, Maria Jo\~{a}o Varanda and Henriques, Pedro Rangel},
  title =	{{CVTool: Automating Content Variants of CVs}},
  booktitle =	{14th Symposium on Languages, Applications and Technologies (SLATE 2025)},
  pages =	{5:1--5:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-387-4},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{135},
  editor =	{Baptista, Jorge and Barateiro, Jos\'{e}},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2025.5},
  URN =		{urn:nbn:de:0030-drops-236855},
  doi =		{10.4230/OASIcs.SLATE.2025.5},
  annote =	{Keywords: Latex CV, CV Versioning, DSL, CV Parsing, CV Templates}
}
Document
Survey
DOI: 10.4230/TGDK.3.1.3
Uncertainty Management in the Construction of Knowledge Graphs: A Survey

Authors: Lucas Jarnac, Yoan Chabot, and Miguel Couceiro

Published in: TGDK, Volume 3, Issue 1 (2025). Transactions on Graph Data and Knowledge, Volume 3, Issue 1

Abstract
Knowledge Graphs (KGs) are a major asset for companies thanks to their great flexibility in data representation and their numerous applications, e.g., vocabulary sharing, Q&A or recommendation systems. To build a KG, it is a common practice to rely on automatic methods for extracting knowledge from various heterogeneous sources. However, in a noisy and uncertain world, knowledge may not be reliable and conflicts between data sources may occur. Integrating unreliable data would directly impact the use of the KG, therefore such conflicts must be resolved. This could be done manually by selecting the best data to integrate. This first approach is highly accurate, but costly and time-consuming. That is why recent efforts focus on automatic approaches, which represent a challenging task since it requires handling the uncertainty of extracted knowledge throughout its integration into the KG. We survey state-of-the-art approaches in this direction and present constructions of both open and enterprise KGs. We then describe different knowledge extraction methods and discuss downstream tasks after knowledge acquisition, including KG completion using embedding models, knowledge alignment, and knowledge fusion in order to address the problem of knowledge uncertainty in KG construction. We conclude with a discussion on the remaining challenges and perspectives when constructing a KG taking into account uncertainty.
Cite as

Lucas Jarnac, Yoan Chabot, and Miguel Couceiro. Uncertainty Management in the Construction of Knowledge Graphs: A Survey. In Transactions on Graph Data and Knowledge (TGDK), Volume 3, Issue 1, pp. 3:1-3:48, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@Article{jarnac_et_al:TGDK.3.1.3,
  author =	{Jarnac, Lucas and Chabot, Yoan and Couceiro, Miguel},
  title =	{{Uncertainty Management in the Construction of Knowledge Graphs: A Survey}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{3:1--3:48},
  ISSN =	{2942-7517},
  year =	{2025},
  volume =	{3},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.3.1.3},
  URN =		{urn:nbn:de:0030-drops-233733},
  doi =		{10.4230/TGDK.3.1.3},
  annote =	{Keywords: Knowledge reconciliation, Uncertainty, Heterogeneous sources, Knowledge graph construction}
}
Document
Academic Track
DOI: 10.4230/OASIcs.SAIA.2024.15
AI Assessment in Practice: Implementing a Certification Scheme for AI Trustworthiness (Academic Track)

Authors: Carmen Frischknecht-Gruber, Philipp Denzel, Monika Reif, Yann Billeter, Stefan Brunner, Oliver Forster, Frank-Peter Schilling, Joanna Weng, and Ricardo Chavarriaga

Published in: OASIcs, Volume 126, Symposium on Scaling AI Assessments (SAIA 2024)

Abstract
The trustworthiness of artificial intelligence systems is crucial for their widespread adoption and for avoiding negative impacts on society and the environment. This paper focuses on implementing a comprehensive certification scheme developed through a collaborative academic-industry project. The scheme provides practical guidelines for assessing and certifying the trustworthiness of AI-based systems. The implementation of the scheme leverages aspects from Machine Learning Operations and the requirements management tool Jira to ensure continuous compliance and efficient lifecycle management. The integration of various high-level frameworks, scientific methods, and metrics supports the systematic evaluation of key aspects of trustworthiness, such as reliability, transparency, safety and security, and human oversight. These methods and metrics were tested and assessed on real-world use cases to dependably verify means of compliance with regulatory requirements and evaluate criteria and detailed objectives for each of these key aspects. Thus, this certification framework bridges the gap between ethical guidelines and practical application, ensuring the safe and effective deployment of AI technologies.
Cite as

Carmen Frischknecht-Gruber, Philipp Denzel, Monika Reif, Yann Billeter, Stefan Brunner, Oliver Forster, Frank-Peter Schilling, Joanna Weng, and Ricardo Chavarriaga. AI Assessment in Practice: Implementing a Certification Scheme for AI Trustworthiness (Academic Track). In Symposium on Scaling AI Assessments (SAIA 2024). Open Access Series in Informatics (OASIcs), Volume 126, pp. 15:1-15:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{frischknechtgruber_et_al:OASIcs.SAIA.2024.15,
  author =	{Frischknecht-Gruber, Carmen and Denzel, Philipp and Reif, Monika and Billeter, Yann and Brunner, Stefan and Forster, Oliver and Schilling, Frank-Peter and Weng, Joanna and Chavarriaga, Ricardo},
  title =	{{AI Assessment in Practice: Implementing a Certification Scheme for AI Trustworthiness}},
  booktitle =	{Symposium on Scaling AI Assessments (SAIA 2024)},
  pages =	{15:1--15:18},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-357-7},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{126},
  editor =	{G\"{o}rge, Rebekka and Haedecke, Elena and Poretschkin, Maximilian and Schmitz, Anna},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SAIA.2024.15},
  URN =		{urn:nbn:de:0030-drops-227554},
  doi =		{10.4230/OASIcs.SAIA.2024.15},
  annote =	{Keywords: AI Assessment, Certification Scheme, Artificial Intelligence, Trustworthiness of AI systems, AI Standards, AI Safety}
}
Document
Survey
DOI: 10.4230/TGDK.2.1.3
Semantic Web: Past, Present, and Future

Authors: Ansgar Scherp, Gerd Groener, Petr Škoda, Katja Hose, and Maria-Esther Vidal

Published in: TGDK, Volume 2, Issue 1 (2024): Special Issue on Trends in Graph Data and Knowledge - Part 2. Transactions on Graph Data and Knowledge, Volume 2, Issue 1

Abstract
Ever since the vision was formulated, the Semantic Web has inspired many generations of innovations. Semantic technologies have been used to share vast amounts of information on the Web, enhance them with semantics to give them meaning, and enable inference and reasoning on them. Throughout the years, semantic technologies, and in particular knowledge graphs, have been used in search engines, data integration, enterprise settings, and machine learning. In this paper, we recap the classical concepts and foundations of the Semantic Web as well as modern and recent concepts and applications, building upon these foundations. The classical topics we cover include knowledge representation, creating and validating knowledge on the Web, reasoning and linking, and distributed querying. We enhance this classical view of the so-called "Semantic Web Layer Cake" with an update of recent concepts that include provenance, security and trust, as well as a discussion of practical impacts from industry-led contributions. We conclude with an outlook on the future directions of the Semantic Web. This is a living document. If you like to contribute, please contact the first author and visit: https://github.com/ascherp/semantic-web-primer
Cite as

Ansgar Scherp, Gerd Groener, Petr Škoda, Katja Hose, and Maria-Esther Vidal. Semantic Web: Past, Present, and Future. In Special Issue on Trends in Graph Data and Knowledge - Part 2. Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 1, pp. 3:1-3:37, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{scherp_et_al:TGDK.2.1.3,
  author =	{Scherp, Ansgar and Groener, Gerd and \v{S}koda, Petr and Hose, Katja and Vidal, Maria-Esther},
  title =	{{Semantic Web: Past, Present, and Future}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{3:1--3:37},
  ISSN =	{2942-7517},
  year =	{2024},
  volume =	{2},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.1.3},
  URN =		{urn:nbn:de:0030-drops-198607},
  doi =		{10.4230/TGDK.2.1.3},
  annote =	{Keywords: Linked Open Data, Semantic Web Graphs, Knowledge Graphs}
}
Document
Position
DOI: 10.4230/TGDK.1.1.5
Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities

Authors: Jiaoyan Chen, Hang Dong, Janna Hastings, Ernesto Jiménez-Ruiz, Vanessa López, Pierre Monnin, Catia Pesquita, Petr Škoda, and Valentina Tamma

Published in: TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1

Abstract
The term life sciences refers to the disciplines that study living organisms and life processes, and include chemistry, biology, medicine, and a range of other related disciplines. Research efforts in life sciences are heavily data-driven, as they produce and consume vast amounts of scientific data, much of which is intrinsically relational and graph-structured. The volume of data and the complexity of scientific concepts and relations referred to therein promote the application of advanced knowledge-driven technologies for managing and interpreting data, with the ultimate aim to advance scientific discovery. In this survey and position paper, we discuss recent developments and advances in the use of graph-based technologies in life sciences and set out a vision for how these technologies will impact these fields into the future. We focus on three broad topics: the construction and management of Knowledge Graphs (KGs), the use of KGs and associated technologies in the discovery of new knowledge, and the use of KGs in artificial intelligence applications to support explanations (explainable AI). We select a few exemplary use cases for each topic, discuss the challenges and open research questions within these topics, and conclude with a perspective and outlook that summarizes the overarching challenges and their potential solutions as a guide for future research.
Cite as

Jiaoyan Chen, Hang Dong, Janna Hastings, Ernesto Jiménez-Ruiz, Vanessa López, Pierre Monnin, Catia Pesquita, Petr Škoda, and Valentina Tamma. Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 5:1-5:33, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{chen_et_al:TGDK.1.1.5,
  author =	{Chen, Jiaoyan and Dong, Hang and Hastings, Janna and Jim\'{e}nez-Ruiz, Ernesto and L\'{o}pez, Vanessa and Monnin, Pierre and Pesquita, Catia and \v{S}koda, Petr and Tamma, Valentina},
  title =	{{Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{5:1--5:33},
  year =	{2023},
  volume =	{1},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.5},
  URN =		{urn:nbn:de:0030-drops-194791},
  doi =		{10.4230/TGDK.1.1.5},
  annote =	{Keywords: Knowledge graphs, Life science, Knowledge discovery, Explainable AI}
}
Document
Position
DOI: 10.4230/TGDK.1.1.2
Large Language Models and Knowledge Graphs: Opportunities and Challenges

Authors: Jeff Z. Pan, Simon Razniewski, Jan-Christoph Kalo, Sneha Singhania, Jiaoyan Chen, Stefan Dietze, Hajira Jabeen, Janna Omeliyanenko, Wen Zhang, Matteo Lissandrini, Russa Biswas, Gerard de Melo, Angela Bonifati, Edlira Vakaj, Mauro Dragoni, and Damien Graux

Published in: TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1

Abstract
Large Language Models (LLMs) have taken Knowledge Representation - and the world - by storm. This inflection point marks a shift from explicit knowledge representation to a renewed focus on the hybrid representation of both explicit knowledge and parametric knowledge. In this position paper, we will discuss some of the common debate points within the community on LLMs (parametric knowledge) and Knowledge Graphs (explicit knowledge) and speculate on opportunities and visions that the renewed focus brings, as well as related research topics and challenges.
Cite as

Jeff Z. Pan, Simon Razniewski, Jan-Christoph Kalo, Sneha Singhania, Jiaoyan Chen, Stefan Dietze, Hajira Jabeen, Janna Omeliyanenko, Wen Zhang, Matteo Lissandrini, Russa Biswas, Gerard de Melo, Angela Bonifati, Edlira Vakaj, Mauro Dragoni, and Damien Graux. Large Language Models and Knowledge Graphs: Opportunities and Challenges. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 2:1-2:38, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{pan_et_al:TGDK.1.1.2,
  author =	{Pan, Jeff Z. and Razniewski, Simon and Kalo, Jan-Christoph and Singhania, Sneha and Chen, Jiaoyan and Dietze, Stefan and Jabeen, Hajira and Omeliyanenko, Janna and Zhang, Wen and Lissandrini, Matteo and Biswas, Russa and de Melo, Gerard and Bonifati, Angela and Vakaj, Edlira and Dragoni, Mauro and Graux, Damien},
  title =	{{Large Language Models and Knowledge Graphs: Opportunities and Challenges}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{2:1--2:38},
  year =	{2023},
  volume =	{1},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.2},
  URN =		{urn:nbn:de:0030-drops-194766},
  doi =		{10.4230/TGDK.1.1.2},
  annote =	{Keywords: Large Language Models, Pre-trained Language Models, Knowledge Graphs, Ontology, Retrieval Augmented Language Models}
}
Document
DOI: 10.4230/OASIcs.ICPEC.2022.9
Integration of Computer Science Assessment into Learning Management Systems with JuezLTI

Authors: Juan V. Carrillo, Alberto Sierra, José Paulo Leal, Ricardo Queirós, Salvador Pellicer, and Marco Primo

Published in: OASIcs, Volume 102, Third International Computer Programming Education Conference (ICPEC 2022)

Abstract
Computer science is a skill that will continue to be in high demand in the foreseeable future. Despite this trend, automated assessment in computer science is often hampered by the lack of systems supporting a wide range of topics. While there is a number of open software systems and programming exercise collections supporting automated assessment, up to this date, there are few systems that offer a diversity of exercises ranging from computer programming exercises to markup and databases languages. At the same time, most of the best-of-breed solutions force teachers and students to alternate between the Learning Management System - a pivotal piece of the e-learning ecosystem - and the tool providing the exercises. This issue is addressed by JuezLTI, an international project whose goal is to create an innovative tool to allow the automatic assessment of exercises in a wide range of computer science topics. These topics include different languages used in computer science for programming, markup, and database management. JuezLTI borrows part of its name from the IMS Learning Tools Interoperability (IMS LTI) standard. With this standard, the tool will interoperate with reference systems such as Moodle, Sakai, Canvas, or Blackboard, among many others. Another contribution of JuezLTI will be a pool of exercises. Interoperability and content are expected to foster the adoption of JuezLTI by many institutions. This paper presents the JuezLTI project, its architecture, and its main components.
Cite as

Juan V. Carrillo, Alberto Sierra, José Paulo Leal, Ricardo Queirós, Salvador Pellicer, and Marco Primo. Integration of Computer Science Assessment into Learning Management Systems with JuezLTI. In Third International Computer Programming Education Conference (ICPEC 2022). Open Access Series in Informatics (OASIcs), Volume 102, pp. 9:1-9:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{carrillo_et_al:OASIcs.ICPEC.2022.9,
  author =	{Carrillo, Juan V. and Sierra, Alberto and Leal, Jos\'{e} Paulo and Queir\'{o}s, Ricardo and Pellicer, Salvador and Primo, Marco},
  title =	{{Integration of Computer Science Assessment into Learning Management Systems with JuezLTI}},
  booktitle =	{Third International Computer Programming Education Conference (ICPEC 2022)},
  pages =	{9:1--9:8},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-229-7},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{102},
  editor =	{Sim\~{o}es, Alberto and Silva, Jo\~{a}o Carlos},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2022.9},
  URN =		{urn:nbn:de:0030-drops-166130},
  doi =		{10.4230/OASIcs.ICPEC.2022.9},
  annote =	{Keywords: programming, interoperability, automatic assessment, programming exercises}
}
Document
Invited Talk
DOI: 10.4230/OASIcs.SLATE.2014.3
Language-Driven Software Development (Invited Talk)

Authors: José-Luis Sierra

Published in: OASIcs, Volume 38, 3rd Symposium on Languages, Applications and Technologies (2014)

Abstract
Language-driven software development consists in applying computer language design and implementation techniques to build conventional software. The keynote reviews two different language- driven development approaches: domain-specific languages (DLSs), and language-oriented architectures (LOAs). The DSL approach focuses on the provision of languages specialized in different application aspects, which are used by developers, and even by domain experts, during application construction and maintenance. The LOA strategy, in its turn, conceives applications them- selves as coordinated collections of language processors, which can be developed using language implementation tools (parser generators, attribute grammar-based systems, etc.). The presentation of the approaches is supported by case studies from the fields of knowledge-based systems, e-Learning, semi-structured data processing, and digital humanities.
Cite as

José-Luis Sierra. Language-Driven Software Development (Invited Talk). In 3rd Symposium on Languages, Applications and Technologies. Open Access Series in Informatics (OASIcs), Volume 38, pp. 3-12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)

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@InProceedings{sierra:OASIcs.SLATE.2014.3,
  author =	{Sierra, Jos\'{e}-Luis},
  title =	{{Language-Driven Software Development}},
  booktitle =	{3rd Symposium on Languages, Applications and Technologies},
  pages =	{3--12},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-68-2},
  ISSN =	{2190-6807},
  year =	{2014},
  volume =	{38},
  editor =	{Pereira, Maria Jo\~{a}o Varanda and Leal, Jos\'{e} Paulo and Sim\~{o}es, Alberto},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2014.3},
  URN =		{urn:nbn:de:0030-drops-45542},
  doi =		{10.4230/OASIcs.SLATE.2014.3},
  annote =	{Keywords: domain-specific languages, language-oriented architectures, parser generators, attribute grammars, application domains}
}
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