10:15-11:15     PRACTICAL MAPREDUCE

Tom White: MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function... View the podcast here...

 11:15-12:15     INTRODUCING APACHE MAHOUT

Isabel Drost: Mahout's goal is to build scalable, Apache licensed machine learning libraries. Initially, the interest is on building out the ten machine learning libraries... View the podcast here...

 12:15-13:15     SCALABLE REASONING ON RDF DOCUMENTS WITH HADOOP AND HBASE

Michele Catasta gave a talk on Scalable Reasoning on RDF Documents with Hadoop and HBase.

 13:15-14:15     THE TERRIER PROJECT

Iadh Ounis: Terrier is a robust and modular Information Retrieval engine. From version 2.2, Terrier supports the indexing of large collections in a Hadoop Map Reduce fashion. This uses the single-pass indexer to index sections of each collection (as batches of files) as map tasks... View the podcast here...

 14:15-15:15     HAVING FUN WITH PAGERANK AND MAPREDUCE

Paolo Castagna: This talk is about implementing PageRank with MapReduce. PageRank is a link analysis algorithm used by the Google Internet search engine that assigns... View the podcast here...

 15:15-16:15     APACHE HBASE

Michael Stack: HBase is the Hadoop database. Its an open-source, distributed, column-oriented store modeled after the Google paper, Bigtable... View the podcast here...

 16:15-17:00     HYPERCUBES IN HBASE

Zohmg is a data store for multi-dimensional time series. it's built on top of hbase and provides tools for data aggregation using hadoop. Data retrieval is designed to be quick; most of the aggregation happens during data import...

 17:00-00:00     HADOOP-1722 AND TYPED BYTES

This talk is about the Hadoop Core - Jira issue with the key HADOOP-1722, which is about Make streaming handle non-utf8 byte array...