Robust Machine Learning Applied to Terascale Astronomical Datasets
| dc.creator | Ball, Nicholas M. | |
| dc.creator | Brunner, Robert J. | |
| dc.creator | Myers, Adam D. | |
| dc.date | 2008-04-21 | |
| dc.date.accessioned | 2026-07-07T09:35:14Z | |
| dc.date.available | 2026-07-07T09:35:14Z | |
| dc.description | We present recent results from the LCDM (Laboratory for Cosmological Data Mining; http://lcdm.astro.uiuc.edu) collaboration between UIUC Astronomy and NCSA to deploy supercomputing cluster resources and machine learning algorithms for the mining of terascale astronomical datasets. This is a novel application in the field of astronomy, because we are using such resources for data mining, and not just performing simulations. Via a modified implementation of the NCSA cyberenvironment Data-to-Knowledge, we are able to provide improved classifications for over 100 million stars and galaxies in the Sloan Digital Sky Survey, improved distance measures, and a full exploitation of the simple but powerful k-nearest neighbor algorithm. A driving principle of this work is that our methods should be extensible from current terascale datasets to upcoming petascale datasets and beyond. We discuss issues encountered to-date, and further issues for the transition to petascale. In particular, disk I/O will become a major limiting factor unless the necessary infrastructure is implemented. | |
| dc.description | 11 pages, 2 figures, uses llncs.cls. To appear in the 9th LCI International Conference on High-Performance Clustered Computing | |
| dc.identifier | https://arxiv.org/abs/0804.3417 | |
| dc.identifier | http://arxiv.org/abs/0804.3417 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/159769 | |
| dc.subject | Astrophysics | |
| dc.subject | Distributed, Parallel, and Cluster Computing | |
| dc.title | Robust Machine Learning Applied to Terascale Astronomical Datasets | |
| dc.type | text |