research-article

Experiences of models@run-time with EMF and CDO

Authors:

Daniel Seybold,

Jörg Domaschka,

Alessandro Rossini,

Christopher B. Hauser,

Frank Griesinger,

Athanasios TsitsipasAuthors Info & Claims

SLE 2016: Proceedings of the 2016 ACM SIGPLAN International Conference on Software Language Engineering

Pages 46 - 56

https://doi.org/10.1145/2997364.2997380

Published: 20 October 2016 Publication History

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Abstract

Model-drivenengineering promotes models and modeltrans-

formations as the primary assets in software development.

The models@run-time approach provides an abstract rep-

resentation of a system at run-time, whereby changes in

the model and the system are constantly reflected on each

other. In this paper, we report on more than three years

of experience with realising models@run-time in scalable

cloud scenarios using a technology stack consisting of the

Eclipse Modelling Framework (EMF) and Connected Data

Objects(CDO).We establish requirements for the three roles

domain-specific language (DSL) designer, developer, and

operator, and compare them against the capabilities of EM-

F/CDO. It turns out that this technology stack is well-suited

for DSL designers, but less recommendable for developers

and even less suited for operators. For these roles, we experi-

enced a steep learning curve and several lacking features that

hinder the implementation of models@run-time in scalable

cloud scenarios. Performance experiences show limitations

for write heavy scenarios with an increasing amount of total

elements. While we do not discourage the use of EMF/CDO

for such scenarios, we recommend that its adoption for sim-

ilar use cases is carefully evaluated until this technology

stack has realised our wish list of advanced features.

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Boronat A(2023)EMF-Syncer: scalable maintenance of view models over heterogeneous data-centric software systems at run timeSoftware and Systems Modeling10.1007/s10270-023-01111-722:6(1949-1968)Online publication date: 12-Jun-2023
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Javed MMuram FHansson HPunnekkat SThane H(2022)Towards dynamic safety assurance for Industry 4.0Journal of Systems Architecture: the EUROMICRO Journal10.1016/j.sysarc.2020.101914114:COnline publication date: 3-Jan-2022
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Information

Published In

SLE 2016: Proceedings of the 2016 ACM SIGPLAN International Conference on Software Language Engineering

October 2016

257 pages

ISBN:9781450344470

DOI:10.1145/2997364

General Chair:
Tijs van der Storm
CWI, Netherlands / University of Groningen, Netherlands
,
Program Chairs:
Emilie Balland
Sensational, Switzerland
,
Daniel Varro
n.n.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 20 October 2016

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https://doi.org/10.1007/s10270-023-01111-7
Javed MMuram FHansson HPunnekkat SThane H(2022)Towards dynamic safety assurance for Industry 4.0Journal of Systems Architecture: the EUROMICRO Journal10.1016/j.sysarc.2020.101914114:COnline publication date: 3-Jan-2022
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https://doi.org/10.1109/MODELS-C53483.2021.00075
Reynolds OGarcía-Domínguez ABencomo NGuerra EIovino L(2020)Automated provenance graphs for [email protected]Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings10.1145/3417990.3419503(1-10)Online publication date: 16-Oct-2020
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Guideline for the definition of EMF metamodels using an Entity-Relationship approach

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Model-driven Engineering in the Development of Ubiquitous Applications: Technologies, Tools and Languages

Guideline for the definition of EMF metamodels using an Entity-Relationship approach

Querying large models efficiently

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