Publications

@article{Giner-Miguelez:SCICO:2024,

title = {DescribeML: A dataset description tool for machine learning},

author = {Joan Giner-Miguelez and Abel G\'{o}mez and Jordi Cabot},

doi = {10.1016/j.scico.2023.103030},

issn = {0167-6423},

year  = {2023},

date = {2023-09-12},

urldate = {2024-01-01},

journal = {Science of Computer Programming},

volume = {231},

pages = {103030},

publisher = {Elsevier BV},

abstract = {Datasets are essential for training and evaluating machine learning models. However, they are also the root cause of many undesirable model behaviors, such as biased predictions. To address this issue, the machine learning community is proposing as a best practice the adoption of common guidelines for describing datasets. However, these guidelines are based on natural language descriptions of the dataset, hampering the automatic computation and analysis of such descriptions. To overcome this situation, we present DescribeML, a language engineering tool to precisely describe machine learning datasets in terms of their composition, provenance, and social concerns in a structured format. The tool is implemented as a Visual Studio Code extension.},

keywords = {Datasets, Domain-Specific Languages (DSLs), Fairness, Machine Learning (ML), Model-Driven Engineering (MDE), Software},

pubstate = {published},

tppubtype = {article}

}

@conference{Giner-Miguelez:MODELS:2022,

title = {DescribeML: A Tool for Describing Machine Learning Datasets},

author = {Joan Giner-Miguelez and Abel G\'{o}mez and Jordi Cabot},

doi = {10.1145/3550356.3559087},

isbn = {9781450394673},

year  = {2022},

date = {2022-11-09},

urldate = {2022-01-01},

booktitle = {Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings},

pages = {22\textendash26},

publisher = {Association for Computing Machinery},

address = {Montreal, Quebec, Canada},

series = {MODELS '22},

abstract = {Datasets play a central role in the training and evaluation of machine learning (ML) models. But they are also the root cause of many undesired model behaviors, such as biased predictions. To overcome this situation, the ML community is proposing a data-centric cultural shift, where data issues are given the attention they deserve, for instance, proposing standard descriptions for datasets.In this sense, and inspired by these proposals, we present a model-driven tool to precisely describe machine learning datasets in terms of their structure, data provenance, and social concerns. Our tool aims to facilitate any ML initiative to leverage and benefit from this data-centric shift in ML (e.g., selecting the most appropriate dataset for a new project or better replicating other ML results). The tool is implemented with the Langium workbench as a Visual Studio Code plugin and published as an open-source.},

keywords = {Datasets, DescribeML, Domain-Specific Languages (DSLs), Fairness, Model-Driven Engineering (MDE)},

pubstate = {published},

tppubtype = {conference}

}

@conference{Giner-Miguelez:RCIS:2022,

title = {Enabling Content Management Systems as an Information Source in Model-Driven Projects},

author = { Joan Giner-Miguelez and Abel G\'{o}mez and Jordi Cabot},

editor = { Renata Guizzardi and Jolita Ralyt\'{e} and Xavier Franch},

doi = {10.1007/978-3-031-05760-1_30},

isbn = {978-3-031-05760-1},

year  = {2022},

date = {2022-05-11},

urldate = {2022-05-11},

booktitle = {Research Challenges in Information Science. RCIS 2022.},

pages = {513--528},

publisher = {Springer International Publishing},

address = {Cham},

series = {Lecture Notes in Business Information Processing},

abstract = {Content Management Systems (CMSs) are the most popular tool when it comes to create and publish content across the web. Recently, CMSs have evolved, becoming headless. Content served by a headless CMS aims to be consumed by other applications and services through REST APIs rather than by human users through a web browser. This evolution has enabled CMSs to become a notorious source of content to be used in a variety of contexts beyond pure web navigation. As such, CMS have become an important component of many information systems. Unfortunately, we still lack the tools to properly discover and manage the information stored in a CMS, often highly customized to the needs of a specific domain. Currently, this is mostly a time-consuming and error-prone manual process.},

keywords = {Datasets, Domain-Specific Languages (DSLs), Machine Learning (ML), MLOPs},

pubstate = {published},

tppubtype = {conference}

}

@conference{Giner-Miguelez:JISBD:2022,

title = {Un lenguaje para definir datasets para machine learning},

author = {Joan Giner-Miguelez and Abel G\'{o}mez and Jordi Cabot},

editor = {A. Go\~{n}i Sarriguren},

url = {http://hdl.handle.net/11705/JISBD/2022/4368},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {Actas de las XXVI Jornadas de Ingenier\'{i}a del Software y Bases de Datos (JISBD 2022)},

publisher = {SISTEDES},

abstract = {Recientes estudios han reportado efectos indeseados y nocivos en modelos de machine learning (ML), en gran parte causados por problemas o limitaciones en los datasets usados para entrenarlos. Esta situaci\'{o}n ha despertado el inter\'{e}s dentro de la comunidad de ML para mejorar los procesos de creaci\'{o}n y compartici\'{o}n de datasets. Sin embargo, hasta la fecha, las propuestas para estandarizar la descripci\'{o}n y formalizaci\'{o}n de los mismos se basan en gu\'{i}as generales en texto natural y que, como tales, presentan limitaciones (precisi\'{o}n, ambig+APw-edad, etc.) y son dif\'{i}ciles de aplicar de una forma (semi)automatizada.En este trabajo proponemos un lenguaje espec\'{i}fico de dominio para describir datasets basado en las propuestas mencionadas. Este lenguaje contribuye a estandarizar los procesos de descripci\'{o}n de los datasets, y pretende ser la base para aplicaciones de formalizaci\'{o}n, b\'{u}squeda y comparaci\'{o}n de estos. Finalmente, presentamos la implementaci\'{o}n de este lenguaje en forma de plug-in para Visual Studio Code.},

keywords = {Datasets, Domain-Specific Languages (DSLs), Machine Learning (ML), MLOPs},

pubstate = {published},

tppubtype = {conference}

}

@article{PAUC:Iglesias-Urkia:2020,

title = {Automatic generation of Web of Things servients using Thing Descriptions},

author = {Markel Iglesias-Urkia and Abel G\'{o}mez and Diego Casado-Mansilla and Aitor Urbieta},

url = {https://rdcu.be/b5GHq},

doi = {10.1007/s00779-020-01413-3},

issn = {1617-4917},

year  = {2020},

date = {2020-07-18},

journal = {Personal and Ubiquitous Computing},

abstract = {Similarly to the standardization effort initiated for the World Wide Web in the 1990s, the World Wide Web Consortium is currently working on the Web of Things (WoT) specification. This initiative aims to tackle current fragmentation in the so-called Internet of Things by using existing Web standards. The ultimate goal is to cope with the increasing number of devices that are being connected to the Internet and to enable interoperability among them. On the other hand, Model-Driven Engineering (MDE) approaches make use of models to raise the abstraction level with the objective of accelerating the software development process, enabling design and code reuse, and increasing software quality.This work proposes to apply MDE techniques to enable the efficient development of WoT servients. Based on the WoT Thing Description specification, this work proposes both a textual-based concrete syntax and a model-based abstract syntax\textemdashboth fully compliant with the WoT specification\textemdashthat enable the generation of WoT servients in C++ with CoAP communication capabilities. This proposal is implemented by a tool that covers the whole development process, which is publicly available under an open source license.},

keywords = {Domain-Specific Languages (DSLs), Internet of Things (IoT), Model-Driven Engineering (MDE), Web of Things (WoT)},

pubstate = {published},

tppubtype = {article}

}

@conference{Iglesias-Urkia:IoT:2019,

title = {Enabling easy Web of Things compatible device generation using a Model-Driven Engineering approach},

author = {Markel Iglesias-Urkia and Abel G\'{o}mez and Diego Casado-Mansilla and Aitor Urbieta},

doi = {10.1145/3365871.3365898},

isbn = {978-1-4503-7207-7},

year  = {2019},

date = {2019-10-22},

booktitle = {Proceedings of the 9th International Conference on the Internet of Things},

pages = {25:1--25:8},

publisher = {ACM},

address = {New York},

series = {IoT 2019},

abstract = {One of the main ongoing standardization efforts of the Internet of Things (IoT) at the application layer is the Web of Things (WoT), which aims to enable interoperability using already existing standards. However, keeping up the design and implementation of IoT applications with the exponentially increasing number of devices being interconnected is costly in workforce resources. Model-Driven Engineering (MDE) approaches increase the level of abstraction using models, and allowing to reuse design and code. This lowers the use of resources for implementing solutions seamlessly. This is why in this work we implement a MDE approach based on the WoT, allowing easy WoT-based device generation. Besides, automated code generation is applied to reduce manual tasks even further. Using the Eclipse Modelling Framework (EMF) and its associated plugins, we provide a way of describing models graphically and generate the code automatically, reducing development and testing time.},

keywords = {Code Generation, Domain-Specific Languages (DSLs), Internet of Things (IoT), Model-Driven Engineering (MDE), Web of Things (WoT)},

pubstate = {published},

tppubtype = {conference}

}

@conference{Ed-douibi:SAC:2016,

title = {EMF-REST: Generation of RESTful APIs from Models},

author = {Hamza Ed-douibi and Javier Luis C\'{a}novas Izquierdo and Abel G\'{o}mez and Massimo Tisi and Jordi Cabot},

doi = {10.1145/2851613.2851782},

isbn = {978-1-4503-3739-7},

year  = {2016},

date = {2016-04-04},

booktitle = {Proceedings of the 31st Annual ACM Symposium on Applied Computing},

pages = {1446--1453},

publisher = {ACM},

address = {New York, NY, USA},

series = {SAC '16},

abstract = {In the last years, there has been an increasing interest for Model-Driven Engineering (MDE) solutions in the Web. Web-based modeling solutions can leverage on better support for distributed management (i.e., the Cloud) and collaboration. However, current modeling environments and frameworks are usually restricted to desktop-based scenarios and therefore their capabilities to move to the Web are still very limited. In this paper we present an approach to generate Web APIs out of models, thus paving the way for managing models and collaborating on them online. The approach, called EMF-REST, takes Eclipse Modeling Framework (EMF) data models as input and generates Web APIs following the REST principles and relying on well-known libraries and standards, thus facilitating its comprehension and maintainability. Also, EMF-REST integrates model and Web-specific features to provide model validation and security capabilities, respectively, to the generated API.},

note = {Pisa, Italia},

keywords = {Domain-Specific Languages (DSLs), Model-Driven Engineering (MDE), Model-Driven Web Engineering (MDWE), REST},

pubstate = {published},

tppubtype = {conference}

}

@conference{DBLP:conf/jisbd/GomezBHCR06,

title = {Definici\'{o}n de operaciones complejas con un lenguaje espec\'{i}fico de dominio en Gesti\'{o}n de Modelos},

author = {Abel G\'{o}mez and Artur Boronat and Luis Hoyos and Jos\'{e} \'{A}. Cars\'{i} and Isidro Ramos},

editor = {Jos\'{e} Riquelme and Pere Botella},

url = {https://abel.gomez.llana.me/wp-content/uploads/2017/11/gomez-jisbd-2006.pdf},

isbn = {84-95999-99-4},

year  = {2006},

date = {2006-10-03},

booktitle = {XI Jornadas de Ingenier\'{i}a del Software y Bases de Datos (JISBD 2006), Octubre 3-6, 2006, Sitges, Barcelona, Spain.},

pages = {215--224},

publisher = {CIMNE},

address = {Barcelona, Spain},

abstract = {La Ingenier\'{i}a dirigida por Modelos permite incrementar la productividad en el proceso de desarrollo software, obteniendo herramientas m\'{a}s interoperables y sencillas de mantener mediante t\'{e}cnicas que elevan el nivel de abstracci\'{o}n. En esta direcci\'{o}n ha aparecido la disciplina «Gesti\'{o}n de Modelos», que proporciona un conjunto de operadores gen\'{e}ricos basados en teor\'{i}a de conjuntos para tratar con modelos. Esta aproximaci\'{o}n muestra su potencia en las capacidades de composicionalidad de los operadores que proporciona. Este art\'{i}culo describe c\'{o}mo proporciona soporte a la definici\'{o}n de operadores complejos una herramienta del marco de la Gesti\'{o}n de Modelos mediante un lenguaje espec\'{i}fico de dominio.},

note = {Sitges, Barcelona, Spain},

keywords = {Domain-Specific Languages (DSLs), Maude, Model Management, Model-Driven Engineering (MDE), MOMENT},

pubstate = {published},

tppubtype = {conference}

}