SCHEDULE: NOV 12-18, 2011

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

SESSION: MapReduce

EVENT TYPE: Paper

TIME: 11:30AM - 12:00PM

AUTHOR(S):Atilla S. Balkir, Ian Foster, Andrey Rzhetsky

ROOM:TCC 305

ABSTRACT:
We study text analysis algorithms that use global optimization methods to compute local characteristics that are consistent with properties of the entire corpus rather than computed locally based on exogenous parameters. In the iterative implementations that we consider, each step both reads and updates a database of parameter values. Motivated by a need for rapid analysis of large corpora, we have developed methods for efficient access to such databases on parallel computers. These methods combine Bloom filters, in-memory caches, and an HBase cluster to reduce communication costs greatly relative to simpler approaches that either fully distribute or fully replicate the database. We also describe how this method can be incorporated into the MapReduce programming model, and illustrate its use within phrase segmentation programs. Our design can achieve considerable run time, latency and storage space improvements relative to other methods. In one phrase segmentation application, we improve performance by a factor of six relative to an HBase-based implementation.

Chair/Author Details:

Atilla S. Balkir - University of Chicago

Ian Foster - University of Chicago/Argonne National Laboratory

Andrey Rzhetsky - University of Chicago/Argonne National Laboratory

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The full paper can be found in the ACM Digital Library