Transcript faloutsos-sdm07-pane..

CMU SCS
SDM’07 Panel
Data Mining Research: Current
Status and Future Opportunities
Christos Faloutsos
CMU
CMU SCS
Questions
Q1: What are future challenges and
opportunities for data mining that are not
presently receiving as much attention as
they deserve?
Q2: Are there things we are doing now that
we should be rethinking in considering
future challenges and opportunities for
data mining?
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Past + current successes
• cross-disciplinarity: DM = Stat, ML, DB
• fascinating apps:
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Bio-informatics
privacy
security
streams
social network mining
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Machine Learning to support Systems Biology:
Subcellular Location - Bob Murphy
Cell Images of
many proteins
Feature Extraction,
Graphical Models,
Clustering of
proteins by pattern
Generative
Models for
each SDM'07
pattern
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Combine to enable
accurate
simulation of cell
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behavior
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Q1: Challenges to focus on
• Scalability – mining Tera and Peta bytes
– stream mining (anomaly, intrusion detection,
sensors)
– graph mining (text/web mining, marketing, ...)
– autonomic systems
– search engines
– national security
– ...
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Scalability
• Google: > 450,000 processors in clusters of
~2000 processors each
Barroso, Dean, Hölzle, “Web Search for a
Planet: The Google Cluster Architecture”
IEEE Micro 2003
• target: hundreds of Tb, to several Peta-bytes
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E.g.: self-* system @ CMU
• >200 nodes
• 40 racks of computing
equipment
• 774kw of power.
• target: 1 PetaByte
• goal: self-correcting, selfsecuring, self-monitoring, self...
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DM for Tera- and Peta-bytes
Two-way street:
<- DM can use such infrastructures to find
patterns
-> DM can help such infrastructures become
self-healing, self-adjusting, ‘self-*’
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Q2: What to do differently
• emphasis on Systems – DM collaboration
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