58,856 research outputs found
Norms, organisations and semantic web services: The ALIVE approach
ALIVE is an EU FP7 STREP whose goal is the
convergence of organisational and normative modelling with and service-oriented architectures (SOAs) using model-driven
software engineering. The project provides a framework for designing and implementing systems, taking into account organisational,
coordination and service perspectives. A key project aspect is the integration of normative systems with live SOAs, through the distributed monitoring of normative state. Here we give a brief overview of the project, explore of the domain from a service context, outline the architecture under construction and sketch the use-cases that illustrate and inform the project.Peer ReviewedPostprint (published version
Model-driven engineering approach to design and implementation of robot control system
In this paper we apply a model-driven engineering approach to designing
domain-specific solutions for robot control system development. We present a
case study of the complete process, including identification of the domain
meta-model, graphical notation definition and source code generation for
subsumption architecture -- a well-known example of robot control architecture.
Our goal is to show that both the definition of the robot-control architecture
and its supporting tools fits well into the typical workflow of model-driven
engineering development.Comment: Presented at DSLRob 2011 (arXiv:cs/1212.3308
Research and Education in Computational Science and Engineering
Over the past two decades the field of computational science and engineering
(CSE) has penetrated both basic and applied research in academia, industry, and
laboratories to advance discovery, optimize systems, support decision-makers,
and educate the scientific and engineering workforce. Informed by centuries of
theory and experiment, CSE performs computational experiments to answer
questions that neither theory nor experiment alone is equipped to answer. CSE
provides scientists and engineers of all persuasions with algorithmic
inventions and software systems that transcend disciplines and scales. Carried
on a wave of digital technology, CSE brings the power of parallelism to bear on
troves of data. Mathematics-based advanced computing has become a prevalent
means of discovery and innovation in essentially all areas of science,
engineering, technology, and society; and the CSE community is at the core of
this transformation. However, a combination of disruptive
developments---including the architectural complexity of extreme-scale
computing, the data revolution that engulfs the planet, and the specialization
required to follow the applications to new frontiers---is redefining the scope
and reach of the CSE endeavor. This report describes the rapid expansion of CSE
and the challenges to sustaining its bold advances. The report also presents
strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie
Designing Software Architectures As a Composition of Specializations of Knowledge Domains
This paper summarizes our experimental research and software development activities in designing robust, adaptable and reusable software architectures. Several years ago, based on our previous experiences in object-oriented software development, we made the following assumption: ‘A software architecture should be a composition of specializations of knowledge domains’. To verify this assumption we carried out three pilot projects. In addition to the application of some popular domain analysis techniques such as use cases, we identified the invariant compositional structures of the software architectures and the related knowledge domains. Knowledge domains define the boundaries of the adaptability and reusability capabilities of software systems. Next, knowledge domains were mapped to object-oriented concepts. We experienced that some aspects of knowledge could not be directly modeled in terms of object-oriented concepts. In this paper we describe our approach, the pilot projects, the experienced problems and the adopted solutions for realizing the software architectures. We conclude the paper with the lessons that we learned from this experience
Towards Product Lining Model-Driven Development Code Generators
A code generator systematically transforms compact models to detailed code.
Today, code generation is regarded as an integral part of model-driven
development (MDD). Despite its relevance, the development of code generators is
an inherently complex task and common methodologies and architectures are
lacking. Additionally, reuse and extension of existing code generators only
exist on individual parts. A systematic development and reuse based on a code
generator product line is still in its infancy. Thus, the aim of this paper is
to identify the mechanism necessary for a code generator product line by (a)
analyzing the common product line development approach and (b) mapping those to
a code generator specific infrastructure. As a first step towards realizing a
code generator product line infrastructure, we present a component-based
implementation approach based on ideas of variability-aware module systems and
point out further research challenges.Comment: 6 pages, 1 figure, Proceedings of the 3rd International Conference on
Model-Driven Engineering and Software Development, pp. 539-545, Angers,
France, SciTePress, 201
Towards guidelines for building a business case and gathering evidence of software reference architectures in industry
Background: Software reference architectures are becoming widely adopted by organizations that need to support the design and maintenance of software applications of a shared domain. For organizations that plan to adopt this architecture-centric approach, it becomes fundamental to know the return on investment and to understand how software reference architectures are designed, maintained, and used. Unfortunately, there is little evidence-based support to help organizations with these challenges.
Methods: We have conducted action research in an industry-academia collaboration between the GESSI research group and everis, a multinational IT consulting firm based in Spain.
Results: The results from such collaboration are being packaged in order to create guidelines that could be used in similar contexts as the one of everis. The main result of this paper is the construction of empirically-grounded guidelines that support organizations to decide on the adoption of software reference architectures and to gather evidence to improve RA-related practices.
Conclusions: The created guidelines could be used by other organizations outside of our industry-academia collaboration. With this goal in mind, we describe the guidelines in detail for their use.Peer ReviewedPostprint (published version
Content-driven design and architecture of E-learning applications
E-learning applications combine content with learning technology systems to support the creation of content and its delivery to the learner. In the future, we can expect the distinction between learning content and its supporting infrastructure to become blurred. Content objects will interact with infrastructure services as independent objects. Our solution to the development of e-learning applications – content-driven design and architecture – is based on content-centric ontological modelling and development of architectures. Knowledge and modelling will play an important role in the development of content and architectures. Our approach integrates content with
interaction (in technical and educational terms) and services (the principle organization for a system architecture), based on techniques from different fields, including software engineering, learning design, and knowledge engineering
Interpretable deep learning for guided structure-property explorations in photovoltaics
The performance of an organic photovoltaic device is intricately connected to
its active layer morphology. This connection between the active layer and
device performance is very expensive to evaluate, either experimentally or
computationally. Hence, designing morphologies to achieve higher performances
is non-trivial and often intractable. To solve this, we first introduce a deep
convolutional neural network (CNN) architecture that can serve as a fast and
robust surrogate for the complex structure-property map. Several tests were
performed to gain trust in this trained model. Then, we utilize this fast
framework to perform robust microstructural design to enhance device
performance.Comment: Workshop on Machine Learning for Molecules and Materials (MLMM),
Neural Information Processing Systems (NeurIPS) 2018, Montreal, Canad
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