Automated and Transparent Model Fragmentation for Persisting Large Models

Scheidgen, Markus; Zubow, Anatolij; Fischer, Joachim; Kolbe, Thomas H.

doi:10.1007/978-3-642-33666-9_8

Cited by 20 publications

(19 citation statements)

References 12 publications

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“…However, different experiences have shown that CDO does not scale well to very large models [21,22,24]. Barmpis and Kolovos [4] suggest that NoSQL databases would provide better scalability and performance than relational databases due to the interconnected nature of models.…”

Section: Related Workmentioning

confidence: 99%

Neo4EMF, A Scalable Persistence Layer for EMF Models

Benelallam¹,

Gómez²,

Sunyé³

et al. 2014

Modelling Foundations and Applications

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Abstract. Several industrial contexts require software engineering methods and tools able to handle large-size artifacts. The central idea of abstraction makes model-driven engineering (MDE) a promising approach in such contexts, but current tools do not scale to very large models (VLMs): already the task of storing and accessing VLMs from a persisting support is currently inefficient. In this paper we propose a scalable persistence layer for the de-facto standard MDE framework EMF. The layer exploits the efficiency of graph databases in storing and accessing graph structures, as EMF models are. A preliminary experimentation shows that typical queries in reverse-engineering EMF models have good performance on such persistence layer, compared to file-based backends.

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Section: Related Workmentioning

confidence: 99%

Neo4EMF, A Scalable Persistence Layer for EMF Models

Benelallam¹,

Gómez²,

Sunyé³

et al. 2014

Modelling Foundations and Applications

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“…The popular EMF provides the means to partition models with proxies, but lacks support for fragmentation strategies (FSs). To improve this, [11] proposes a non-formal persistence framework for EMF to fragment models along annotated metamodel compositions. Our theory is formal and provides a powerful notion of fragmentation regions that allows metamodel-defined fragmentations along our container primitive of clusters.…”

Section: Related Workmentioning

confidence: 99%

Fragmenta: A theory of fragmentation for MDE

Amálio

Lara

Guerra

2015

2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS)

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: Abstract-Model-Driven Engineering (MDE) promotes models throughout development. However, models may become large and unwieldy even for small to medium-sized systems. This paper tackles the MDE challenges of model complexity and scalability. It proposes FRAGMENTA, a theory of modular design that breaks down overall models into fragments that can be put together to build meaningful wholes, in contrast to classical MDE approaches that are essentially monolithic. The theory is based on an algebraic description of models, fragments and clusters based on graphs and morphisms. The paper's novelties include: (i) a mathematical treatment of fragments and a seaming mechanism of proxies to enable inter-fragment referencing, (ii) fragmentation strategies, which prescribe a fragmentation structure to model instances, (iii) FRAGMENTA's support for both top-down and bottom-up design, and (iv) our formally proved result that shows that inheritance hierarchies remain well-formed (acyclic) globally when fragments are composed provided some local fragment constraints are met.

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“…First, we built a framework that allows us to collect and represent sensor data in well structured, strictly typed EMF models [15]. Second, we extended EMF's resource API to persist large amounts of sensor data to distributed databases [14,13]. Thirdly, we use model transformation techniques to implement algorithms (work in progress).…”

Section: Emf-based Tools For Experimentationmentioning

confidence: 99%

“…We developed EMF-Fragments [14,13], an EMF-based persistence framework that automatically fragments a model into many resources and maps resources and their URIs to values and keys in a distributed key-value store (as in NoSQL database, e.g. Hadoop's HBase [5]).…”

Section: Emf-fragmentsmentioning

confidence: 99%

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EMF modeling in traffic surveillance experiments

Scheidgen

Zubow

2012

Proceedings of the Modelling of the Physical World Workshop

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We use a wireless sensor network equipped with acceleration sensors to measure seismic waves caused by rolling traffic. In this paper, we report on our experiences in applying an EMF-based data infrastructure to these experiments. We built an experimentation infrastructure that replaces unstructured text-file based management of data with a modelbased approach. We use EMF to represent sensor data and corresponding analysis results; we use an extension of EMF's resource API to persist data in a database; and we use model transformations to describe data analysis. We conclude that a model based approach leads to safer, better documented, and more reproducible experiments.

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Automated and Transparent Model Fragmentation for Persisting Large Models

Cited by 20 publications

References 12 publications

Neo4EMF, A Scalable Persistence Layer for EMF Models

Neo4EMF, A Scalable Persistence Layer for EMF Models

Fragmenta: A theory of fragmentation for MDE

EMF modeling in traffic surveillance experiments

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