Nov 09

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

    [...] Read: MapReduce & Hadoop Algorithms in Academic Papers [...]

  2. Mapreduce & Hadoop Algorithms in Academic Papers (5th update – Nov 2011) « Another Word For It Says:

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

  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………

  5. 09CST-FYP交流平台 » 数据分析与数据挖掘相关资源整理 Says:

    [...] 1. Mapreduce & Hadoop Algorithms in Academic Papers (5th update – Nov 2011) [...]

  6. Hadoop Learning Resources | hadoop4u Says:

    […]… […]

  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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