Comparative analysis of data persistence technologies for large-scale models

Barmpis, Konstantinos; Kolovos, Dimitrios S.

doi:10.1145/2467307.2467314

Cited by 25 publications

(25 citation statements)

References 8 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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“…In summary, the Neo4j graph database supports better extensibility and more efficient queries [20], [21] of connected data. Both are consequences of its lightweight, schema-free data model.…”

Section: The Nosql Movementmentioning

confidence: 99%

Position Paper: Towards a Minimalistic Modeling Paradigm

Kövesdán¹,

Asztalos²,

Lengyel³

2014

IJMO

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Abstract-Model-Driven Software Engineering (MDSE) has put a great emphasis on modeling to deal with the rapidly growing complexity of software systems. This leads to that models become more complex and detailed as they evolve. In contrast to the vision that such models are able to deal with this complexity, there are several cases in which simple models better support the requirements. In this paper, three technology trends are presented. The success of these trends lays in using lightweight models. Of course, inherently complex systems tend to use complex models but the main goal is to focus modeling on supporting requirements and avoid adding unnecessary details. After the explanation of the technology trends, their key success factors are identified. By these success factors, we outline some considerations that facilitate maintaining the complexity of models low.Index Terms-Modeling, model-driven software engineering, agility. I. INTRODUCTION Model-Driven Software Engineering (MDSE)is a widely applied method in software engineering, which uses the model as a first-class artifact in the engineering process. The model is leveraged in software development instead of focusing on algorithmic concepts. The Model-Driven Architecture (MDA) methodology, standardized by the Object Management Group (OMG) even tries to avoid programming in the conventional sense and use modeling instead as a primary means of software development. The expansion of these technologies has emphasized the importance of the model. Nowadays, there is a big effort in creating more accurate and complete models. Often we ask ourselves, how we efficiently model problem domain D to capture all of the significant details.At the same time, complexity of software systems grows at a rapid pace. The result of this is that models become always bigger and more complex. Their purpose is to facilitate software development but big and complex models are very rigid and make future changes difficult. They also have a negative impact on scalability [3]. There is ongoing research on how to deal with distributed models and parallelized 

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“…In [4] persistence of models in graph-databases is evaluated. Graph databases are based on the paradigm of indexfree adjacency.…”

Section: Related Workmentioning

confidence: 99%

“…In [25], we observed that part of source does not scale with the number of referenced objects, because all references are stored (and have to be completely loaded) as part of the source object. Others [19,4] have observed that relations on the other hand require complex and expensive database operations (e.g. joins) when models are accessed.…”

Section: Introductionmentioning

confidence: 99%

Reference representation techniques for large models

Scheidgen

2013

Proceedings of the Workshop on Scalability in Model Driven Engineering

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If models consist of more and more objects, time and space required to process these models becomes an issue. To solve this we can employ different existing frameworks that use different model representations (e.g. trees in XMI or relational data with CDO). Based on the observation that these frameworks reach different performance measures for different operations and different model characteristics, we rise the question if and how different model representations can be combined to mitigate performance issues of individual representations.In this paper, we analyze different techniques to represent references, which are one important aspect to process large models efficiently. We present the persistence framework EMF-Fragments, which combines the representation of references as source-object contained sets of target-objects (e.g. in XMI) within the representation as relations similar to those in relational databases (e.g. with CDO). We also present a performance evaluation for both representations and discuss the use of both representations in three applications: models for source-code repositories, scientific data, and geo-spatial data.

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Comparative analysis of data persistence technologies for large-scale models

Cited by 25 publications

References 8 publications

Neo4EMF, A Scalable Persistence Layer for EMF Models

Neo4EMF, A Scalable Persistence Layer for EMF Models

Position Paper: Towards a Minimalistic Modeling Paradigm

Reference representation techniques for large models

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