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Data Management Challenges of Data-Intensive Scientific Workflows

Published: 19 May 2008 Publication History

Abstract

Scientific workflows play an important role in today’s science. Many disciplines rely on workflow technologies to orchestrate the execution of thousands of computational tasks. Much research to-date focuses on efficient, scalable, and robust workflow execution, especially in distributed environments. However, many challenges remain in the area of data management related to workflow creation, execution, and result management. In this paper we examine some of these issues in the context of the entire workflow lifecycle.

Cited By

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  • (2024)Optimizing data regeneration and storage with data dependency for cloud scientific workflow systemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121984238:PDOnline publication date: 15-Mar-2024
  • (2023)TaskVine: Managing In-Cluster Storage for High-Throughput Data Intensive WorkflowsProceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis10.1145/3624062.3624277(1978-1988)Online publication date: 12-Nov-2023
  • (2021)Designing for Recommending Intermediate States in A Scientific Workflow Management SystemProceedings of the ACM on Human-Computer Interaction10.1145/34571455:EICS(1-29)Online publication date: 29-May-2021
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Published In

cover image Guide Proceedings
CCGRID '08: Proceedings of the 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid
May 2008
817 pages
ISBN:9780769531564

Publisher

IEEE Computer Society

United States

Publication History

Published: 19 May 2008

Author Tags

  1. data management
  2. distributed computing
  3. scientific workflows

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Cited By

View all
  • (2024)Optimizing data regeneration and storage with data dependency for cloud scientific workflow systemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121984238:PDOnline publication date: 15-Mar-2024
  • (2023)TaskVine: Managing In-Cluster Storage for High-Throughput Data Intensive WorkflowsProceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis10.1145/3624062.3624277(1978-1988)Online publication date: 12-Nov-2023
  • (2021)Designing for Recommending Intermediate States in A Scientific Workflow Management SystemProceedings of the ACM on Human-Computer Interaction10.1145/34571455:EICS(1-29)Online publication date: 29-May-2021
  • (2019)Waveform Signal Entropy and Compression Study of Whole-Building Energy DatasetsProceedings of the Tenth ACM International Conference on Future Energy Systems10.1145/3307772.3328285(58-67)Online publication date: 15-Jun-2019
  • (2018)Improved Algorithm for Finding the Minimum Cost of Storing and Regenerating Datasets in Multiple CloudsComputing and Combinatorics10.1007/978-3-319-94776-1_35(416-427)Online publication date: 2-Jul-2018
  • (2017)PANORAMAInternational Journal of High Performance Computing Applications10.1177/109434201559451531:1(4-18)Online publication date: 1-Jan-2017
  • (2017)MaDaTSProceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing10.1145/3078597.3078611(41-52)Online publication date: 26-Jun-2017
  • (2017)A novel strategy for efficient temporal verification with intersectant situation in scientific workflowProceedings of the Australasian Computer Science Week Multiconference10.1145/3014812.3014854(1-5)Online publication date: 30-Jan-2017
  • (2017)"They're all going out to something weird"Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing10.1145/2998181.2998325(995-1008)Online publication date: 25-Feb-2017
  • (2017)Improved algorithms for intermediate dataset storage in a cloud-based dataflowTheoretical Computer Science10.1016/j.tcs.2016.05.042657:PA(48-53)Online publication date: 2-Jan-2017
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