TeraScope: distributed visual data mining of terascale data sets over photonic networks

TeraScope is a framework and a suite of tools for interactively browsing and visualizing large terascale data sets. Unique to TeraScope is its utilization of the Optiputer paradigm to treat distributed computer clusters as a single giant computer, where the dedicated optical networks that connect the clusters serve as the computer's system bus. TeraScope explores one aspect of the Optiputer architecture by employing a distributed pool of memory, called LambdaRAM, that serves as a massive data cache for supporting parallel data mining and visualization algorithms.

[1]  Robert L. Grossman,et al.  Simple Available Bandwidth Utilization Library for High-Speed Wide Area Networks , 2005, The Journal of Supercomputing.

[2]  Robert L. Grossman,et al.  A High Performance Implementation of the Data Space Transfer Protocol (DSTP) , 1999, Large-Scale Parallel Data Mining.

[3]  Daniel A. Keim,et al.  Information Visualization and Visual Data Mining , 2002, IEEE Trans. Vis. Comput. Graph..

[4]  Usama M. Fayyad,et al.  Knowledge Discovery in Databases: An Overview , 1997, ILP.

[5]  Joel H. Saltz,et al.  Processing large-scale multi-dimensional data in parallel and distributed environments , 2002, Parallel Comput..

[6]  Ian T. Foster,et al.  The data grid: Towards an architecture for the distributed management and analysis of large scientific datasets , 2000, J. Netw. Comput. Appl..

[7]  Vithanage Pemajayantha Special Canonical Models for Multidimensional Data Analysis for Distributed Computing and Data Minin , 2002 .

[8]  Joel H. Saltz,et al.  Exploration and Visualization of Very Large Datasets with the Active Data Repository , 2001 .

[9]  Abraham Silberschatz,et al.  Operating Systems Concepts , 2005 .

[10]  William Schroeder,et al.  The Visualization Toolkit: An Object-Oriented Approach to 3-D Graphics , 1997 .

[11]  Joel H. Saltz,et al.  Visualization of Large Data Sets with the Active Data Repository , 2001, IEEE Computer Graphics and Applications.

[12]  Hans-Peter Kriegel,et al.  Visualization Techniques for Mining Large Databases: A Comparison , 1996, IEEE Trans. Knowl. Data Eng..

[13]  Kai Li,et al.  IVY: A Shared Virtual Memory System for Parallel Computing , 1988, ICPP.

[14]  Pak Chung Wong,et al.  Guest Editor's Introduction: Visual Data Mining , 1999, IEEE Computer Graphics and Applications.

[15]  William Frawley,et al.  Knowledge Discovery in Databases , 1991 .