Master Thesis MSTR-2017-80

BibliographyYussupov, Vladimir: Concepts for handling heterogeneous data transformation logic and their integration with TraDE middleware.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Master Thesis No. 80 (2017).
127 pages, english.

The concept of programming-in-the-Large became a substantial part of modern computerbased scientific research with an advent of web services and the concept of orchestration languages. While the notions of workflows and service choreographies help to reduce the complexity by providing means to support the communication between involved participants, the process still remains generally complex. The TraDE Middleware and underlying concepts were introduced in order to provide means for performing the modeled data exchange across choreography participants in a transparent and automated fashion. However, in order to achieve both transparency and automation, the TraDE Middleware must be capable of transforming the data along its path. The data transformation’s transparency can be difficult to achieve due to various factors including the diversity of required execution environments and complicated configuration processes as well as the heterogeneity of data transformation software which results in tedious integration processes often involving the manual wrapping of software. Having a method of handling data transformation applications in a standardized manner can help to simplify the process of modeling and executing scientific service choreographies with the TraDE concepts applied. In this master thesis we analyze various aspects of this problem and conceptualize an extensible framework for handling the data transformation applications. The resulting prototypical implementation of the presented framework provides means to address data transformation applications in a standardized manner.

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Department(s)University of Stuttgart, Institute of Architecture of Application Systems
Superviser(s)Leymann, Prof. Frank; Hahn, Michael
Entry dateMay 29, 2019
   Publ. Institute   Publ. Computer Science