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A Distributed Look-up Architecture for Text Mining Applications using MapReduce.

Atilla Soner Balkir1, Ian Foster2, Andrey Rzhetsky3

  • 1Department of Computer Science, University of Chicago.

Proceedings of the ... International Symposium on High Performance Distributed Computing
|October 31, 2014
PubMed
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This study addresses scalability challenges in text mining by proposing a novel multi-layered look-up architecture. This approach optimizes distributed parameter management for large datasets, improving performance in Hadoop clusters.

Area of Science:

  • Computer Science
  • Data Science
  • Natural Language Processing

Background:

  • Text mining statistical models require iterative parameter access and updates.
  • Large datasets lead to parameter-rich models, causing scalability issues with naive parallel implementations.
  • Maintaining distributed look-up tables for model parameters is a key challenge.

Purpose of the Study:

  • To evaluate existing coordination alternatives for worker nodes in Hadoop clusters.
  • To propose a new multi-layered look-up architecture for parameter management.
  • To optimize text mining scalability for large corpora.

Main Methods:

  • Evaluation of existing coordination methods in Hadoop clusters.
  • Development of a multi-layered look-up architecture.

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  • Exploitation of power-law distribution of n-gram counts.
  • Integration of Bloom Filter, in-memory cache, and HBase cluster.

    • Main Results:

    • Naive parallel implementations fail to scale for parameter-rich text mining models.
    • The proposed multi-layered architecture offers an optimized solution for specific problem domains.
    • The architecture effectively manages distributed look-up tables for model parameters.
    • Leveraging power-law distributions enhances the efficiency of the look-up system.

    Conclusions:

    • The novel multi-layered look-up architecture significantly improves scalability for parameter-rich text mining models.
    • The solution is optimized for domains exhibiting power-law characteristics in data, such as n-gram counts.
    • The integration of Bloom Filter, cache, and HBase provides a robust and scalable parameter management system.
    • This work advances distributed computing techniques for large-scale text analysis.