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Streamgen: a UML-based tool for developing streaming applications

Published: 27 May 2018 Publication History

Abstract

Distributed streaming applications, i.e. applications that process massive and potentially infinite streams of data, are becoming increasingly popular in order to tame at the same time the velocity and the volume of Big Data. Designing and developing distributed streaming applications is currently difficult because it involves the employment of 1) complex programming paradigms to deal with the unboundedness of data streams together with 2) distributed streaming engines, each coming with its own APIs. To address the above shortcomings, in this tool demo paper we present StreamGen, a model-driven tool aiming at simplifying the development of distributed streaming applications. StreamGen provides (i) a UML profile to add streaming-specific concepts to standard UML Class Diagrams and (ii) a model-to-text transformation to automatically generate the application code starting from UML models.

References

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Mathieu Colas, Ingo Finck, Jerome Buvat, Roopa Nambiar, and Rishi Raj Singh. 2015. Cracking the Data Conundrum: How Successful Companies Make Big Data Operational. Technical Report. Capgemini consulting. url: https://www.capgemini-consulting.com/cracking-the-data-conundrum.
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Akidau et al. 2015. The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing. Proceedings of the VLDB Endowment (2015).
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Carbone et al. 2015. Apache Flink<sup>TM</sup>: Stream and Batch Processing in a Single Engine. IEEE Data Eng. Bull. (2015), 28--38.
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Michele Guerriero, Saeed Tajfar, Damian A. Tamburri, and Elisabetta Di Nitto. Towards a Model-driven Design Tool for Big Data Architectures. In BIGDSE '16 Proceedings.
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cover image ACM Conferences
MiSE '18: Proceedings of the 10th International Workshop on Modelling in Software Engineering
May 2018
87 pages
ISBN:9781450357357
DOI:10.1145/3193954
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 May 2018

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Author Tags

  1. UML
  2. big data
  3. domain-specific modeling languages
  4. model-driven development
  5. streaming applications

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  • Research-article

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ICSE '18
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Overall Acceptance Rate 13 of 30 submissions, 43%

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