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A Cloud-Based Workflow Management Solution for Collaborative Analytics

  • Henry Kasim
  • Terence Hung
  • Xiaorong Li
  • William-Chandra Tjhi
  • Sifei Lu
  • Long Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7221)

Abstract

The concept of collaborative analytics is to accommodate reuse and collaboration in data analysis process through sharing of analytics methods, algorithms, and computation resources. However, realizing collaborative analytics is challenging due to the large data sets, high throughput and computational intensive requirements. In this demonstration, we present a cloud-based workflow management solution that allows collaborative analytics to run in the cloud computing environment. Our solution provides sharing of analytics resources, recommendation of analytic workflows, dynamic scheduling and provisioning for scalable data analytics, high availability through fault-tolerance, real-time monitoring and tracking of collaborative analytics status. Examples of a generic data mining analysis and climate change analytics are given to show that our work can be applied for a wide variety of study in the real-life world.

Keywords

Cloud Environment Dynamic Schedule Cloud Computing Environment Hybrid Heuristic Collaborative Analytic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Rapidminer – Analytical ETL, Data Mining, and Predictive Reporting, http://rapid-i.com/
  2. 2.
    Castiello, C., Castellano, G., Fanelli, A.M.: Meta-data: Characterization of Input Features for Meta-learning. In: Torra, V., Narukawa, Y., Miyamoto, S. (eds.) MDAI 2005. LNCS (LNAI), vol. 3558, pp. 457–468. Springer, Heidelberg (2005)CrossRefGoogle Scholar
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    Rahman, M., Li, X., Palit, H.: Hybrid Heuristic for Scheduling Data Analytics Workflow Applications in Hybrid Cloud Environment. In: Proc. High-Performance Grid and Cloud Computing Workshop 2011, USA, May 16-20 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Henry Kasim
    • 1
  • Terence Hung
    • 1
  • Xiaorong Li
    • 1
  • William-Chandra Tjhi
    • 1
  • Sifei Lu
    • 1
  • Long Wang
    • 1
  1. 1.Institute of High Performance Computing, Agency for ScienceTechnology and Research (A*STAR)Singapore

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