1. Spiros Papadimitriou Jimeng Sun IBM T.J. Watson Research Center Hawthorne, NY, USA Reporter: Nai-Hui, Ku

2.  Introduction  Related Work  Distributed Mining Process  Co-clustering Huge Datasets  Experiments  Conclusions

3.  Problems  Huge datasets  Natural sources of data are impure form  Proposed Method  A comprehensive Distributed Co-clustering (DisCo) solution  Using Hadoop  DisCo is a scalable framework under which various co-clustering algorithms can be implemented

4.  Map-Reduce framework  employs a distributed storage cluster  block-addressable storage  a centralized metadata server  a convenient data access  storage API for Map-Reduce tasks

5.  Co-clustering  Algorithm cluster shapes checkerboard partitions single bi-cluster Exclusive row and column partitions overlapping partitions  Optimization criteria code length

6. Identifying the source and obtaining the data Transform raw data into the appropriate format for data analysis Visual results, or turned into the input for other applications.

7.  Data pre-processing  Processing 350 GB raw network event log Needs over 5 hours to extract source/destination IP pairs  Achieve much better performance on a few commodity nodes running Hadoop  Setting up Hadoop required minimal effort

8.  Specifically for co-clustering, there are two main preprocessing tasks: Building the graph from raw data Pre-computing the transpose  During co-clustering optimization, we need to iterate over both rows and columns.  Need to pre-compute the adjacency lists for both the original graph as well as its transpose

9.  Definitions and overview  Matrices are denoted by boldface capital letters  Vectors are denoted by boldface lowercase letters  a ij :the (i, j)-th element of matrix A  Co-clustering algorithms employs a checkerboard the original adjacency matrix  a grid of sub- matrices  An m x n matrix, a co-clustering is a pair of row and column labeling vectors  r(i):the i-th row of the matrix  G: the k×ℓ group matrix A A a a

10.  g pq gives the sufficient statistics for the (p, q) sub-matrix

11.  Map function

12.  Reduce function

13.  Global sync

14.  Setup  39 nodes  Two dual-core processors  8GM RAM  Linux RHEL4  4Gbps Ethernets  SATA, 65MB/sec or roughly 500 Mbps  The total capacity of our HDFS cluster was just 2.4 terabytes  HDFS block size was set to 64MB (default value)  JAVA  Sun JDK version 1.6.0_03

15.  The pre-processing step on the ISS data  Default values  39 nodes  6 concurrent maps per node  5 reduce tasks  256MB input split size

17.  Using relatively low-cost components  I/O rates that exceed those of high-performance storage systems.  Performance scales almost linearly with the number of machines/disks.