Mapreduce & Hadoop Algorithms in Academic Papers (5th update – Nov 2011)

The prior update of this posting was in May, and a lot has happened related to Mapreduce and Hadoop since then, e.g.
1) big software companies have started offering hadoop-based software (Microsoft and Oracle), 2) Hadoop-startups have raised record amounts, and 3) nosql-landscape becoming increasingly datawarehouse’ish and sql’ish with the focus on high-level data processing platforms and query languages.

Personally I have rediscovered Hadoop Pig and combine it with UDFs and streaming as my primary way to implement mapreduce algorithms here in Atbrox.

Best regards,
Amund Tveit (

Changes from the prior postings is that this posting only includes _new_ papers (2011):

Artificial Intelligence/Machine Learning/Data Mining

Bioinformatics/Medical Informatics

Image and Video Processing

Statistics and Numerical Mathematics

Search and Information Retrieval

Sets & Graphs


Social Networks

Spatial Data Processing

Text Processing

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7 Responses to Mapreduce & Hadoop Algorithms in Academic Papers (5th update – Nov 2011)

  1. Pingback: 30 Hadoop and Big Data Spelunkers Worth Following | My Blog

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  3. Nikzad says:

    For whom ther are interested in MapReduce, these two papers may be intersting:
    1) “A Study on Using Uncertain Time Series Matching Algorithms in Map-Reduce Applications”

    2) “MapReduce Implementation of Prestack Kirchhoff Time Migration (PKTM) on Seismic Data”

  4. Karthikeyan says:

    can any one help me to find the coding or methodology for hadoop clustering in text mining? please………

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  7. Ratnesh says:

    Thanks! Hadoop enables resilient, distributed processing of massive unstructured data sets across commodity computer clusters, in which each node of the cluster includes its own storage. MapReduce serves two essential functions: It parcels out work to various nodes within the cluster or map, and it organizes and reduces the results from each node into a cohesive answer to a query. More at

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