4,649 research outputs found
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
Ontology-based patterns for the integration of business processes and enterprise application architectures
Increasingly, enterprises are using Service-Oriented Architecture (SOA) as an approach to Enterprise Application Integration (EAI). SOA has the potential to bridge
the gap between business and technology and to improve the reuse of existing applications and the interoperability with new ones. In addition to service architecture
descriptions, architecture abstractions like patterns and styles capture design knowledge and allow the reuse of successfully applied designs, thus improving the quality of
software. Knowledge gained from integration projects can be captured to build a repository of semantically enriched, experience-based solutions. Business patterns identify the interaction and structure between users, business processes, and data.
Specific integration and composition patterns at a more technical level address enterprise application integration and capture reliable architecture solutions. We use an
ontology-based approach to capture architecture and process patterns. Ontology techniques for pattern definition, extension and composition are developed and their
applicability in business process-driven application integration is demonstrated
Ontology-based modelling of architectural styles
The conceptual modelling of software architectures is of central importance for the quality of a software system. A rich modelling language is required to integrate the different aspects of architecture modelling, such as architectural styles, structural and behavioural modelling, into a coherent framework. Architectural styles are often neglected in software architectures. We propose an ontological approach for architectural style modelling based on description logic as an abstract, meta-level modelling instrument. We introduce a framework for style definition and style combination. The application of the
ontological framework in the form of an integration into existing architectural description notations is illustrated
An autonomous satellite architecture integrating deliberative reasoning and behavioural intelligence
This paper describes a method for the design of autonomous spacecraft, based upon behavioral approaches to intelligent robotics. First, a number of previous spacecraft automation projects are reviewed. A methodology for the design of autonomous spacecraft is then presented, drawing upon both the European Space Agency technological center (ESTEC) automation and robotics methodology and the subsumption architecture for autonomous robots. A layered competency model for autonomous orbital spacecraft is proposed. A simple example of low level competencies and their interaction is presented in order to illustrate the methodology. Finally, the general principles adopted for the control hardware design of the AUSTRALIS-1 spacecraft are described. This system will provide an orbital experimental platform for spacecraft autonomy studies, supporting the exploration of different logical control models, different computational metaphors within the behavioral control framework, and different mappings from the logical control model to its physical implementation
Ontology-based composition and matching for dynamic cloud service coordination
Recent cross-organisational software service offerings, such as cloud computing, create higher integration needs.
In particular, services are combined through brokers and mediators, solutions to allow individual services to collaborate and their interaction to be coordinated are required. The need to address dynamic management - caused by cloud and on-demand environments - can be addressed through service coordination based on ontology-based composition and matching techniques. Our solution to composition and matching utilises a service coordination space that acts as a passive infrastructure for collaboration where users submit requests that are then selected and taken on by providers. We discuss the information models and the coordination principles of such a collaboration environment in terms of an ontology and its underlying description logics. We provide ontology-based solutions for structural composition of descriptions and matching between requested and provided services
Using fuzzy logic to integrate neural networks and knowledge-based systems
Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems
Using Ontologies for the Design of Data Warehouses
Obtaining an implementation of a data warehouse is a complex task that forces
designers to acquire wide knowledge of the domain, thus requiring a high level
of expertise and becoming it a prone-to-fail task. Based on our experience, we
have detected a set of situations we have faced up with in real-world projects
in which we believe that the use of ontologies will improve several aspects of
the design of data warehouses. The aim of this article is to describe several
shortcomings of current data warehouse design approaches and discuss the
benefit of using ontologies to overcome them. This work is a starting point for
discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure
Cloud service localisation
The essence of cloud computing is the provision of software
and hardware services to a range of users in dierent locations. The aim of cloud service localisation is to facilitate the internationalisation and localisation of cloud services by allowing their adaption to dierent locales.
We address the lingual localisation by providing service-level language translation techniques to adopt services to dierent languages and regulatory localisation by providing standards-based mappings to achieve regulatory compliance with regionally varying laws, standards and regulations. The aim is to support and enforce the explicit modelling of
aspects particularly relevant to localisation and runtime support consisting of tools and middleware services to automating the deployment based on models of locales, driven by the two localisation dimensions.
We focus here on an ontology-based conceptual information model that integrates locale specication in a coherent way
LittleDarwin: a Feature-Rich and Extensible Mutation Testing Framework for Large and Complex Java Systems
Mutation testing is a well-studied method for increasing the quality of a
test suite. We designed LittleDarwin as a mutation testing framework able to
cope with large and complex Java software systems, while still being easily
extensible with new experimental components. LittleDarwin addresses two
existing problems in the domain of mutation testing: having a tool able to work
within an industrial setting, and yet, be open to extension for cutting edge
techniques provided by academia. LittleDarwin already offers higher-order
mutation, null type mutants, mutant sampling, manual mutation, and mutant
subsumption analysis. There is no tool today available with all these features
that is able to work with typical industrial software systems.Comment: Pre-proceedings of the 7th IPM International Conference on
Fundamentals of Software Engineerin
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