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Parallelising Harvesting

  • Hussein Suleman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4312)

Abstract

Metadata harvesting has become a common technique to transfer a stream of data from one metadata repository or digital library system to another. As collections of metadata, and their associated digital objects, grow in size, the ingest of these items at the destination archive can take a significant amount of time, depending on the type of indexing or post-processing that is required. This paper discusses an approach to parallelise the post-processing of data in a small cluster of machines or a multi-processor environment, while not increasing the burden on the source data provider. Performance tests have been carried out on varying architectures and the results indicate that this technique is indeed promising for some scenarios and can be extended to more computationally-intensive ingest procedures. In general, the technique presents a new approach for the construction of harvest-based distributed or component-based digital libraries, with better scalability than before.

Keywords

Digital Library Data Provider Disk Access Beowulf Cluster High Computational Load 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hussein Suleman
    • 1
  1. 1.Department of Computer ScienceUniversity of Cape TownRondeboschSouth Africa

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