Software Architecture for Big Data and the Cloud by Unknown

Author:Unknown
Language: eng
Format: epub
Published: 0101-01-01T00:00:00+00:00

Chapter 12

Architecting Cloud Services for the Digital Me in a Privacy-Aware Environment

Robert Eikermann; Markus Look; Alexander Roth; Berhard Rumpe; Andreas Wortmann Software Engineering, RWTH Aachen University, Aachen, Germany

Abstract

Modern cloud-based service architectures have to cope with requirements arising from handling big data such as integrating heterogeneous data sources (variety), storing the large amount of data (volume), keeping up with the frequency of data (velocity), and tolerating errors and faults within the data (veracity). Development of new services must be fast and efficient by reusing already existing services. Reuse and composition of services enable value-added services. Incorporating individual end-user preferences in the service design is important, but raises new challenges regarding privacy. In fact, reuse, composition to value-added services, and fulfilling privacy requirements are hindered by multiple challenges and necessary, yet expensive and error-prone, repetitive tasks. Integration of heterogeneous data sources demands for the identification of a common data model and integration of, typically heterogeneous third-party, data sources. This usually entails that users have to provide much of their personal data to the service, thus losing control over their data. Apart from the loss of control, users also have to give the same content to different services multiple times. In case the content changes they have to update it in several places. The developer has to deal with these problems for every new service under development. We discuss the technical constituents of modern cloud-based service architectures. For each constituent of the modern cloud-based service architecture, we identify target technology-specific parts, parts containing the services' logic, and parts specific to the using service. For those, we identify the possibilities for a generative approach. This classification of parts enables us to analyze what parts of such a modern cloud-service architecture can be automatically generated or at least systematically derived.

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