A. Zimmermann, M. Pretz, G. Zimmermann, D. Firesmith, Ilia Petrov
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The underlying architecture reference model can be used to support semantic analysis and program comprehension of service-oriented Big Data Applications. Enterprise Services Computing is the current trend for powerful large-scale information systems, which increasingly converge with Cloud Computing environments. In this paper we combine architectures for services with cloud computing. We propose a new integration model for service-oriented Enterprise Architectures on basis of ESARC - Enterprise Services Architecture Reference Cube, which is our previous developed service-oriented enterprise architecture classification framework, with MFESA - Method Framework for Engineering System Architectures - for the design of service-oriented enterprise architectures, and the systematic development, diagnostics and optimization of architecture artifacts of service-oriented cloud-based enterprise systems for Big Data applications.","PeriodicalId":376599,"journal":{"name":"2013 17th IEEE International Enterprise Distributed Object Computing Conference Workshops","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"48","resultStr":"{\"title\":\"Towards Service-Oriented Enterprise Architectures for Big Data Applications in the Cloud\",\"authors\":\"A. 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Towards Service-Oriented Enterprise Architectures for Big Data Applications in the Cloud
Applications with Service-oriented Enterprise Architectures in the Cloud are emerging and will shape future trends in technology and communication. The development of such applications integrates Enterprise Architecture and Management with Architectures for Services & Cloud Computing, Web Services, Semantics and Knowledge-based Systems, Big Data Management, among other Architecture Frameworks and Software Engineering Methods. In the present work in progress research, we explore Service-oriented Enterprise Architectures and application systems in the context of Big Data applications in cloud settings. Using a Big Data scenario, we investigate the integration of Services and Cloud Computing architectures with new capabilities of Enterprise Architectures and Management. The underlying architecture reference model can be used to support semantic analysis and program comprehension of service-oriented Big Data Applications. Enterprise Services Computing is the current trend for powerful large-scale information systems, which increasingly converge with Cloud Computing environments. In this paper we combine architectures for services with cloud computing. We propose a new integration model for service-oriented Enterprise Architectures on basis of ESARC - Enterprise Services Architecture Reference Cube, which is our previous developed service-oriented enterprise architecture classification framework, with MFESA - Method Framework for Engineering System Architectures - for the design of service-oriented enterprise architectures, and the systematic development, diagnostics and optimization of architecture artifacts of service-oriented cloud-based enterprise systems for Big Data applications.