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TOSCA4Mashups: enhanced method for on-demand data mashup provisioning

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Computer Science - Research and Development

Abstract

Nowadays, the amount of data increases tremendously. Extracting information and generating knowledge from this data is a great challenge. To cope with this issue – oftentimes referred to as big data problem – we need effective means for efficient data integration, data processing, and data analysis. To enable flexible, explorative and ad-hoc data processing, several data mashup approaches and tools have been developed in the past. One of these tools is FlexMash – a data mashup tool developed at the University of Stuttgart. By offering domain-specific graphical modeling as well as a pattern-based execution, FlexMash enables usage by a wide range of users, both domain experts and technical experts. The core idea of FlexMash is a flexible execution of data mashups using different, user-requirement-dependent execution components. In this paper, we present a new approach for on-demand, automated provisioning of these components in a cloud computing environment using the Topology and Orchestration Specification for Cloud Applications. This enables many advantages for mashup execution such as scalability, availability and cost savings.

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Notes

  1. http://pipes.yahoo.com/pipes/.

  2. http://intel.ly/1BW2crD.

  3. http://pic.dhe.ibm.com/infocenter/mashhelp/v3/.

  4. https://puppetlabs.com/.

  5. https://www.chef.io/.

  6. https://github.com/hirmerpl/FlexMash.

  7. www.cloudcycle.org.

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Authors and Affiliations

  1. Institute of Parallel and Distributed Systems, University of Stuttgart, Universitaetsstr. 38, D-70569, Stuttgart, Germany

    Pascal Hirmer & Bernhard Mitschang

Authors
  1. Pascal Hirmer
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  2. Bernhard Mitschang
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Corresponding author

Correspondence to Pascal Hirmer.

Additional information

This work is supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) within the project SitOPT (Grant 610872).

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Hirmer, P., Mitschang, B. TOSCA4Mashups: enhanced method for on-demand data mashup provisioning. Comput Sci Res Dev 32, 291–300 (2017). https://doi.org/10.1007/s00450-016-0330-7

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  • DOI: https://doi.org/10.1007/s00450-016-0330-7

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