Eugene's ARGUS work (2004-2006)
The ARGUS project was a joint research project involving the Carnegie
Mellon University and DYNAMiX
Technologies. Jaime Carbonell
at Carnegie Mellon was a principal investigator for this project, and Phil
Hayes at DYNAMiX Technologies was a co-principal investigator.
The purpose was to develop techniques for identification of both known
and surprising patterns in large-scale databases, and apply these techniques
to homeland security challenges. This work involved three main directions:
I was working on the problem of identifying approximate matches to known
patterns in large-scale databases, which involved indexing of large collections
of structured records, representation of known patterns as database queries,
and retrieval of approximate matches to given queries.
In-memory and on-disk indexing of large collections of structured records
Fast identification of matches for SQL-style queries
in a stream of new incoming records
Application of clustering techniques to identify surprising changes in
Eugene Fink, Aaron Goldstein, Philip Hayes, and Jaime G. Carbonell.
for approximate matches in large databases. In Proceedings of the
International Conference on Systems, Man, and Cybernetics, 2004. See PostScript,
or conference talk.
B. Cenk Gazen, Jaime G. Carbonell, Philip J. Hayes, Chun Jin, and Eugene
Fink. Hypothesis formation and tracking in ARGUS.
Principal Investigator Meeting, 2004. See
or conference talk.
Jaime G. Carbonell, Eugene Fink, Chun Jin, B. Cenk Gazen, Santosh Ananthraman,
Philip J. Hayes, Ganesh Mani, and Dwight Dietrich. Exploring massive
structured data in ARGUS. NIMD
Principal Investigator Meeting, 2005. See PostScript,
Jaime G. Carbonell, Eugene Fink, Chun Jin, B. Cenk Gazen, Johny Mathew,
Abhay Saxena, Vini Satish, Santosh Ananthraman, Dwight Dietrich, and Ganesh
Mani. Scalable data exploration and novelty detection. NIMD
Principal Investigator Meeting, 2006. See PostScript,
detection and profile tracking from massive data (30 minutes). Presentation
at the Sixth SIAM International Conference on Data Mining, April 21, 2006.
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