Research and development within the MONDO project was completed at the end of April 2016. The following public reports describing the new technologies from the project addressing scalable collaborative modelling are available for download.
Prototype front-end for construction of large DSLs and fine-tuning for large instance models
Describes a prototype meta-modelling tool which has the following goals: (a) to facilitate the development of large Domain-Specific Languages (DSLs) by the composition of meta-modelling patterns, and (b) to generate a customized modelling environment that allows the hierarchical organization of instance models according to predefined fragmentation strategies.
Prototype tool supporting scalable concrete visual syntax
Describes a prototype meta-modelling tool (DSL-tao) with the capability to produce scalable visual modelling environments. The generated environments are complemented by a tool (SAMPLER) with the ability to explore large models through a catalogue of abstractions and navigation strategies.
Integrated tools supporting the development processes
Describes tool support for different activities needed for constructing DSMLs following sound engineering principles, including organizing DSML requirements using a flexible notation with DSL-tao, support for DSML requirements in the form of drawings/sketches with metaBUP, and testing the meta-model design using three different languages supported by metaBEST.
Guidelines for the different development processes
Describes methodological guidelines for three processes for constructing Domain-Specific Languages (DSLs): 1) top-down approach that follows the traditional flow from requirements to design and test; 2) example-based or bottom-up approach where the meta-model is constructed via example sketches which are taken as requirements for the DSL; and 3) a zig-zag approach that combines the two.
Public set of transformation benchmarks
Presents the benchmarks and their corresponding artifacts that will be used to evaluate the MONDO query and transformation engines. The report introduces the benchmarks, their originating context and their representative value in the query and transformation landscape.
Scalable query and transformation engine
The reports describes a reactive paradigm for programming model transformations along with a reactive model-transformation language, and also addresses streaming model transformations that represent a novel class of transformations dealing with models whose elements are continuously produced or modified by a background process.
Cloud-enabled query and transformation engine
Describes an approach to automatically distribute the execution of model transformations written in a popular Model Transformation language, ATL, on top of a well-known distributed programming model, MapReduce, including the extensions to the ATL transformation engine to enable distribution.
Primitives and Patterns for Collaborative Modeling
Presents various collaboration primitives and patterns for offline (e.g. SVN-based) and online strategies (e.g. collaborative modeling authoring sessions), also aiming to provide consistent management techniques for collaborative modeling adapted from version control systems including locking, transaction handling with commit, conflict management and resolution.
Secure Interface for Collaborative Modeling
Reports on investigations in the field of access control presenting a general architecture for granting security access in collaborative modelling scenarios. Moreover, a way to derive secure views for the clients according to complex access control policies is described, along with a security access language over queries for models.
Prototype Tool for Collaborative Modeling
Reports on the Prototype Tool Support of the MONDO Collaboration Framework, and in particular, the final design decisions, refinements and deviations. Additionally, short case studies demonstrate the applicability of the proposed solutions to MONDO usage scenarios.
Data Persistence Technology Evaluation Report
Presents the process and results of benchmarking candidate data stores that can underpin the heterogeneous model indexing framework developed in the project. The obtained results indicate that the Neo4J Property Graph Database delivers the best performance in both persistence and querying when bulk insertion is used.
Heterogeneous Model Management Framework
Presents the foundations, interfaces and organisation of the heterogeneous model management framework that underpins the scalable model indexing framework implemented in MONDO. The final version of the scalable model indexing framework supports both Neo4j and OrientDB using the common database abstraction layer.
Model Indexing Framework
Describes the motivation for scalable model indexing, and presents a high-level view of the requirements imposed on Hawk, the structure of a Hawk model index and the architecture that integrates its various components. The design that implements the architecture is then described in further detail.
High-Performance Model Persistence Format
Presents three tools developed within MONDO that implement complementary solutions to the scalability issues in the current use of XMI: 1) SmartSAX performs static analysis of queries written in EPL; 2) Thrift API for the Hawk indexer provides its own serialization method for sending models over the network; and 3) NeoEMF persistence layer that can replace XMI within EMF for storage and querying supporting both map-based and graph-based representations
MONDO Integrated Platform
Presents the final state of the MONDO platform, organized into cloud and client components, discusses how they interoperate with each other, and presents the final version of the web-service API of the cloud-based part of the platform.