[Hama Wiki] Trivial Update of "FrontPage" by udanax

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- [http://incubator.apache.org/hama Hama] is a distributed matrix computation package currently in incubation with Apache. It is a library of matrix operations for large-scale processing and development environments as well as a [http://wiki.apache.org/hadoop/HadoopMapReduce Map/Reduce] framework (See also, [http://wiki.apache.org/hadoop/Hamburg Hamburg], a framework based on Bulk Synchronous Parallel) for a large-scale numerical analysis and data mining, that need the intensive computation power of matrix inversion, e.g., linear regression, PCA, SVM and etc. It will be useful for many scientific applications, e.g., physics computations, linear algebra, computational fluid dynamics, statistics, graphic rendering and many more. 
+ [http://incubator.apache.org/hama Hama] (means a hippopotamus in Korean) is a distributed scientific package on Hadoop for massive matrix and graph data. It is currently in incubation with Apache. The main goal of Hama is to provide computational tools for data-intensive scientific and industrial areas. It consists of two packages, which are the matrix package and the graph package.
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+ The matrix package (means a hippopotamus in Korean) is a library of matrix operations on a Map/Reduce framework for a large-scale numerical analysis and data mining, that need the intensive computation power of matrix inversion, e.g., linear regression, PCA, SVM and etc. It will be useful for many scientific applications, e.g., physics computations, linear algebra, computational fluid dynamics, statistics, graphic rendering and many more.
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+ The graph package, called Angrapa, is an large-scale graph data management framework for analytical processing. It is still an ongoing project. It will employ massive parallelism on Hadoop. It aims to achieve the scalability for tera bytes or peta bytes graph data. Angrapa will be used in a variety of scientific and industrial areas, such as data mining, machine learning, information retrieval, bioinformatics, and social networks, required to process large-scale graph data.
  
   * Scientific simulation and modeling 
    * Matrix-vector/[:MatrixMultiply:matrix-matrix multiply]