Architecture for Systems Integration
Code
10484
Academic unit
Faculdade de Ciências e Tecnologia
Department
Departamento de Engenharia Electrotécnica
Credits
6.0
Teacher in charge
Ricardo Luís Rosa Jardim Gonçalves
Weekly hours
4
Teaching language
Português
Objectives
Provide students to understand the problem of interoperability between systems and develop skills in:
- Architectures for the integration of systems, general characterization of systems for modeling and integration; identification of the main blocks and characterization of the necessary functionalities; methodologies for developing strategies for integration and development of integrated systems.
- Standards for data modeling and processes: use of frameworks, technologies and associated methodologies, analysis of cases and applications
- Methodologies and technologies for integration of sistems: characterization, specification of translators, direct and indirect methods, architectures. Model Driven Architecture / Model Driven Engineering / Model Driven Interoperability (MDA / MDE / MDI)
- Model-morphisms, mapping between models and data, translators, transformations and systems interfacing. Meta models and multilevel integration models and data. Facilitators and automatic code generators.
- Syntactic and semantic interoperability. Explicit and tacit knowledge. Semantic annotations in data models. Ontologies to support the development of solutions for interoperability.
- Open architectures for interoperability: centralized versus distributed solutions, linear versus hierarchical, standardized interfaces for data access.
- Conformance testing and interoperability checking between systems. Interoperability between hardware systems, middleware and software.
- The challenge of the Architectures for the Integration of Systems in the advent of the Internet of Things (IoT). Use of Raspberry Pi devices, Arduino, Gadgeteer, Zolertia in the development of interoperable solutions.
- Architectures and middleware platforms (eg, Janus) and vocational operating systems (eg, Contiki, TinyOS).
- The Internet, its services and the Cloud interoperability in networked systems.
- Analysis of Cases: Development of advanced systems for the interoperability of systems using LabView; Developing solutions with cloud services for data acquisition in BlueMix environment. Using National Instruments DAQ interface with smart sensors.
Subject matter
Provide students to understand the problem of interoperability between systems and develop skills in:
- Architectures for the integration of systems, general characterization of systems for modeling and integration; identification of the main blocks and characterization of the necessary functionalities; methodologies for developing strategies for integration and development of integrated systems.
- Standards for data modeling and processes: use of frameworks, technologies and associated methodologies, analysis of cases and applications
- Methodologies and technologies for integration of sistems: characterization, specification of translators, direct and indirect methods, architectures. Model Driven Architecture / Model Driven Engineering / Model Driven Interoperability (MDA / MDE / MDI)
- Model-morphisms, mapping between models and data, translators, transformations and systems interfacing. Meta models and multilevel integration models and data. Facilitators and automatic code generators.
- Syntactic and semantic interoperability. Explicit and tacit knowledge. Semantic annotations in data models. Ontologies to support the development of solutions for interoperability.
- Open architectures for interoperability: centralized versus distributed solutions, linear versus hierarchical, standardized interfaces for data access.
- Conformance testing and interoperability checking between systems. Interoperability between hardware systems, middleware and software.
- The challenge of the Architectures for the Integration of Systems in the advent of the Internet of Things (IoT). Use of Raspberry Pi devices, Arduino, Gadgeteer, Zolertia in the development of interoperable solutions.
- Architectures and middleware platforms (eg, Janus) and vocational operating systems (eg, Contiki, TinyOS).
- The Internet, its services and the Cloud interoperability in networked systems.
- Analysis of Cases: Development of advanced systems for the interoperability of systems using LabView; Developing solutions with cloud services for data acquisition in BlueMix environment. Using National Instruments DAQ interface with smart sensors.