Big Data or Data-Intensive applications (DIAs) seek to mine, manipulate, extract or otherwise exploit the potential intelligence hidden behind Big Data. However, several practitioner surveys remark that DIAs potential is still untapped because of very difficult and costly design, quality assessment and continuous refinement. To address the above shortcoming, we propose the use of a UML domain-specific modeling language or profile specifically tailored to support the design, assessment and continuous deployment of DIAs. This article illustrates our DIA-specific profile and outlines its usage in the context of DIA performance engineering and deployment. For DIA performance engineering, we rely on the Apache Hadoop technology, while for DIA deployment, we leverage the TOSCA language. We conclude that the proposed profile offers a powerful language for data-intensive software and systems modeling, quality evaluation and automated deployment of DIAs on private or public clouds.

A UML Profile for the Design, Quality Assessment and Deployment of Data-intensive Applications

Guerriero M.;Di Nitto E.;
2019-01-01

Abstract

Big Data or Data-Intensive applications (DIAs) seek to mine, manipulate, extract or otherwise exploit the potential intelligence hidden behind Big Data. However, several practitioner surveys remark that DIAs potential is still untapped because of very difficult and costly design, quality assessment and continuous refinement. To address the above shortcoming, we propose the use of a UML domain-specific modeling language or profile specifically tailored to support the design, assessment and continuous deployment of DIAs. This article illustrates our DIA-specific profile and outlines its usage in the context of DIA performance engineering and deployment. For DIA performance engineering, we rely on the Apache Hadoop technology, while for DIA deployment, we leverage the TOSCA language. We conclude that the proposed profile offers a powerful language for data-intensive software and systems modeling, quality evaluation and automated deployment of DIAs on private or public clouds.
Apache Hadoop; Big Data; Data-intensive applications; Model-driven deployment; Performance assessment; Profile; Software design; TOSCA language; UML
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1120891
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