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Dynamic variability support in workflow-based systems: an evaluation of the LateVa framework

Published: 13 April 2015 Publication History

Abstract

Context-awareness and adaptability are perceived critical for variability-aware workflow-based systems, which often require efficient process execution. However, existing approaches supporting dynamic variability do not report performance metrics gained from the process configuration phase. To overcome this situation, we present a quantitative evaluation of the LateVa framework to understand how the runtime variability may impact the standard process instance execution. Through an initial empirical study and comparable evaluation metrics, we evaluate the scalability and computation time of the framework using a synthetic scenario.

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cover image ACM Conferences
SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing
April 2015
2418 pages
ISBN:9781450331968
DOI:10.1145/2695664
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 the author(s) 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: 13 April 2015

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Author Tags

  1. context-awareness
  2. late binding
  3. performance metrics
  4. runtime variability
  5. workflow-based systems

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  • Short-paper

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SAC 2015
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SAC 2015: Symposium on Applied Computing
April 13 - 17, 2015
Salamanca, Spain

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SAC '15 Paper Acceptance Rate 291 of 1,211 submissions, 24%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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The 40th ACM/SIGAPP Symposium on Applied Computing
March 31 - April 4, 2025
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