Speakers
Dr
David Malon
(Argonne National Laboratory)Dr
Peter Van Gemmeren
(Argonne National Laboratory)
Description
At a data rate of 200 hertz, event metadata records ("TAGs," in ATLAS parlance)
provide fertile grounds for development and evaluation of tools for scalable data mining.
It is easy, of course, to apply HEP-specific selection or classification rules to event records
and to label such an exercise "data mining," but our interest is different.
Advanced statistical methods and tools such as classification, association rule mining,
and cluster analysis are common outside the high energy physics community. These tools can prove
useful, not necessarily for discovery physics, but for learning about our data, our detector, and our software.
A fixed and relatively simple schema makes TAG export to other storage technologies such as
HDF5 straightforward. This simplifies the task of exploiting very-large-scale parallel platforms
such as Argonne National Laboratory's BlueGene/P, currently the largest supercomputer in the world
for open science, in the development of scalable tools for data mining. Using a domain-neutral
scientific data format may also enable us to take advantage of existing data mining components
from other communities.
There is, further, a substantial literature on the topic of one-pass algorithms and stream
mining techniques, and such tools may be inserted naturally at various points in the event data
processing and distribution chain.
This paper describes early experience with event metadata records from ATLAS simulation
and commisioning as a testbed for scalable data mining tool development and evaluation.
Authors
Dr
David Malon
(Argonne National Laboratory)
Dr
Peter Van Gemmeren
(Argonne National Laboratory)
