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Cached Sufficient Statistics for Automated Mining and Discovery from Massive Data Sources
journal contribution
posted on 1999-01-01, 00:00 authored by Andrew Moore, Jeff Schneider, Brigham Anderson, Scott Davies, Paul Komarek, Mary Soon Lee, Marina Meila, Remi Munos, Kary Myers, Pan PellegThere many massive databases in industry and science. There are also many ways that decision
makers, scientists, and the public need to interact with these data sources. Wide ranging statistics
and machine learning algorithms similarly need to query databases, sometimes millions of times
for a single inference. With millions or billions of records (e.g. biotechnology databases,
inventory management systems, astrophysics sky surveys, corporate sales information, science lab data
repositories) this can be intractable using current algorithms.
The Auton lab (at Carnegie Mellon University) and Schenley Park Research Inc. (a start-
up company), both jointly run by Andrew Moore and Jeff Schneider, are concerned with the
fundamental computer science of making very advanced data analysis techniques computationally
feasible for massive datasets.