Evaluation of Contemporary Graph Databases for Efficient Persistence of Large-Scale Models.

Abstract

Scalability in Model-Driven Engineering (MDE) is often a bottleneck for industrial applications. Industrial scale models need to be persisted in a way that allows for their seamless and efficient manipulation, often by multiple stakeholders simultaneously. This paper compares the conventional and commonly used persistence mechanisms in MDE with novel approaches such as the use of graph-based NoSQL databases; Prototype integrations of Neo4J and OrientDB with EMF are used to compare with relational database, XMI and document-based NoSQL database persistence mechanisms. It also compares and benchmarks two approaches for querying models persisted in graph databases to measure and compare their relative performance in terms of memory usage and execution time.

Stress-Testing Centralised Model Stores

“…Tools like the Eclipse Modeling Framework (EMF) [4], ModelCVS [5], Modelio 1 and MagicDraw 2 all use XML-based model serialization. While this approach offers a structured platform-independent way for storing models, it has been shown by various works such as [3,6] to lack scalability as it requires loading the entire text file in order to retrieve any information needed from the model.…”

“…These model elements conformed to the Java Development Tools AST (JD-TAST) metamodel, which is described in works such as [3] or [7]. Some of the types within the JDTAST metamodel include the TypeDeclarations that represent Java classes and interfaces, the MethodDeclarations that represent Java methods, and the Modifiers that represent Java modifiers on the methods (such as static or public).…”

“…Several ways in which MDE practice could learn from widely-used programming environments to handle large models better were pointed out in [2], with a strong focus on the need for modularity in modelling languages to improve scalability and simplify collaboration. Three categories of scalability issues in MDE were identified in [3]: model persistence issues, model querying and transformation issues, and collaborative modelling issues.…”

Towards Efficient Loading of Change-Based Models

“…Connected Data Objects (CDO) [19], provides support for database-backed model persistence as well as collaboration facilities, < 0.05 M ean = average, SD = standard deviation, t = t-test's t-value, df = degree of freedom, p-value = significance, M = the unit is megabytes but its adoption necessitates the use of a separate version control system in the software development process (e.g. a Git repository for code and a CDO repository for models), which introduces fragmentation and administration challenges [20]. Similar challenges arise in relation to other model-specific version control systems such as EMFStore [21].…”

Exploiting RDF Open Data Using NoSQL Graph Databases

“…For example, social networks, and variety of scientific and business application tend to be easy to manage if data were stored and queried as graphs. NoSQL graph database engines are recognised as being scalable [2] and efficient for some complex data management operations.…”