Publications

@conference{Gomez:ECMFA:2017,

title = {On the Opportunities of Scalable Modeling Technologies: An Experience Report on Wind Turbines Control Applications Development},

author = {Abel G\'{o}mez and Xabier Mendialdua and G\'{a}bor Bergmann and Jordi Cabot and Csaba Debreceni and Antonio Garmendia and Dimitrios S. Kolovos and Juan de Lara and Salvador Trujillo},

editor = {Anthony Anjorin and Hu\'{a}scar Espinoza},

doi = {10.1007/978-3-319-61482-3_18},

isbn = {978-3-319-61482-3},

year  = {2017},

date = {2017-06-20},

booktitle = {Modelling Foundations and Applications: 13th European Conference, ECMFA 2017, Held as Part of STAF 2017, Marburg, Germany, July 19-20, 2017, Proceedings},

volume = {10376},

pages = {300--315},

publisher = {Springer International Publishing},

series = {Lecture Notes in Computer Science},

abstract = {Scalability in modeling has many facets, including the ability to build larger models and domain specific languages (DSLs) efficiently. With the aim of tackling some of the most prominent scalability challenges in Model-based Engineering (MBE), the MONDO EU project developed the theoretical foundations and open-source implementation of a platform for scalable modeling and model management. The platform includes facilities for building large DSLs, for splitting large models into sets of smaller interrelated fragments, and enables modelers to construct and refine complex models collaboratively, among other features.},

keywords = {Experience Report, Model-Driven Engineering (MDE), MONDO, Scalability},

pubstate = {published},

tppubtype = {conference}

}

@article{Daniel:SciCo:2017,

title = {NeoEMF: A multi-database model persistence framework for very large models},

author = {Gwendal Daniel and Gerson Suny\'{e} and Amine Benelallam and Massimo Tisi and Yoann Vernageau and Abel G\'{o}mez and Jordi Cabot},

doi = {10.1016/j.scico.2017.08.002},

issn = {0167-6423},

year  = {2017},

date = {2017-01-01},

journal = {Science of Computer Programming},

volume = {149},

number = {Supplement C},

pages = {9 - 14},

abstract = {The growing role of Model Driven Engineering (MDE) techniques in industry has emphasized scalability of existing model persistence solutions as a major issue. Specifically, there is a need to store, query, and transform very large models in an efficient way. Several persistence solutions based on relational and NoSQL databases have been proposed to achieve scalability. However, they often rely on a single data store, which suits a specific modeling activity, but may not be optimized for other use cases. This paper presents NeoEMF, a tool that tackles this issue by providing a multi-database model persistence framework. Tool website: http://www.neoemf.com},

note = {Special Issue on MODELS'16},

keywords = {Model Persistence, NeoEMF, Scalability, Very Large Models (VLMs)},

pubstate = {published},

tppubtype = {article}

}

@conference{Daniel:MODELS:2016,

title = {NeoEMF: a Multi-database Model Persistence Framework for Very Large Models},

author = {Gwendal Daniel and Gerson Suny\'{e} and Amine Benelallam and Massimo Tisi and Yoann Vernageau and Abel G\'{o}mez and Jordi Cabot},

editor = {Juan de Lara and Peter J. Clarke and Mehrdad Sabetzadeh},

url = {http://ceur-ws.org/Vol-1725/demo1.pdf},

issn = {1613-0073},

year  = {2016},

date = {2016-11-11},

booktitle = {Proceedings of the MoDELS 2016 Demo and Poster Sessions co-located with ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems (MoDELS 2016), Saint-Malo, France, October 2-7, 2016.},

volume = {1725},

pages = {1-7},

publisher = {CEUR Workshop Proceedings},

address = {Saint-Malo, France},

abstract = {The growing use of Model Driven Engineering (MDE) techniques in industry has emphasized scalability of existing model persistence solutions as a major issue. Specifically,

there is a need to store, query, and transform very large models in an efficient way.

Several persistence solutions based on relational and NoSQL databases have been proposed to achieve scalability. However, existing solutions often rely on a single data store, which suits a specific modeling activity, but may not be optimized for other use cases.

In this article we present NeoEMF, a multi-database model persistence framework able to store very large models in key-value stores, graph databases, and wide column databases. We introduce NeoEMF core features, and present the different data stores and their applications. NeoEMF is open source and available online.},

keywords = {Model Persistence, NeoEMF, Scalability, Very Large Models (VLMs)},

pubstate = {published},

tppubtype = {conference}

}