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Job Provenance – Insight into Very Large Provenance Datasets

Software Demonstration
  • Aleš Křenek
  • Luděk Matyska
  • Jiří Sitera
  • Miroslav Ruda
  • František Dvořák
  • Jiří Filipovič
  • Zdeněk Šustr
  • Zdeněk Salvet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5272)

Abstract

Following the job-centric monitoring concept, Job Provenance (JP) service organizes provenance records on the per-job basis. It is designed to manage very large number of records, as was required in the EGEE project where it was developed originally.

The quantitative aspect is also a focus of the presented demonstration. We show JP capability to retrieve data items of interest from a large dataset of full records of more than 1 million of jobs, to perform non-trivial transformation on those data, and organize the results in such a way that repeated interactive queries are possible.

The application area of the demo is derived from that of previous Provenance Challenges. Though the topic of the demo — a computational experiment — is arranged rather artificially, the demonstration still delivers its main message that JP supports non-trivial transformations and interactive queries on large data sets.

Keywords

Hippocampus Volume Grid Environment Main Message Interactive Query Pilot Application 
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.

References

  1. 1.
    Dvořák, F., et al.: gLite job provenance. In: Moreau, L., Foster, I. (eds.) IPAW 2006. LNCS, vol. 4145, pp. 246–253. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Křenek, A., et al.: gLite job provenance—a job-centric view. Concurrency and Computation: Practice and Experience 20(5) (2007) doi: 10.1002/cpe.1252 Google Scholar
  3. 3.
    Křenek, A., et al.: Multiple ligand trajectory docking study —semiautomatic analysis of molecular dynamics simulations using EGEE gLite services. In: Proc. Euromicro Conference on Parallel Distributed and network-based Processing (2008)Google Scholar
  4. 4.
    Schovancová, J., et al.: VO AUGER large scale Monte Carlo simulations using the EGEE grid environment. In: 3rd EGEE User Forum, Clermont-Ferrand, France (2008)Google Scholar
  5. 5.
    Křenek, A., et al.: Experimental evaluation of job provenance in ATLAS environment. J. Phys.: Conf. Series (accepted, 2007)Google Scholar
  6. 6.
    Head, D., et al.: Frontal-hippocampal double dissociation between normal aging and Alzheimer’s disease. Celebral Cortex 15(6), 732–739 (2005)CrossRefGoogle Scholar
  7. 7.
    Matyska, L., et al.: Job tracking on a grid—the Logging and Bookkeeping and Job Provenance services. Technical Report 9/2007, CESNET (2007), http://www.cesnet.cz/doc/techzpravy

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Aleš Křenek
    • 1
    • 2
  • Luděk Matyska
    • 1
    • 2
  • Jiří Sitera
    • 1
  • Miroslav Ruda
    • 1
    • 2
  • František Dvořák
    • 1
  • Jiří Filipovič
    • 1
  • Zdeněk Šustr
    • 1
  • Zdeněk Salvet
    • 1
    • 2
  1. 1.CESNET z.s.p.o.Praha 6Czech Republic
  2. 2.Institute of Computer ScienceMasaryk UniversityBrnoCzech Republic

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