Mining Large Heterogeneous Graphs using Cray s Urika

Conference · Tue Jan 01 04:00:00 EST 2013

OSTI ID:1096972

Sukumar, Sreenivas R ^[1]; Bond, Nathaniel A ^[1]

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1. ORNL

Pattern discovery and predictive modeling from seemingly related Big Data represented as massive, ad-hoc, heterogeneous networks (e.g., extremely large graphs with complex, possibly unknown structure) is an outstanding problem in many application domains. To address this problem, we are designing graph-mining algorithms capable of discovering relationship-patterns from such data and using those discovered patterns as features for classification and predictive modeling. Specifically, we are: (i) exploring statistical properties, mechanics and generative models of behavior patterns in heterogeneous information networks, (ii) developing novel, automated and scalable graph-pattern discovery algorithms and (iii) applying our relationship-analytics (data science + network science) expertise to domains spanning healthcare to homeland security.

• Research Organization: Oak Ridge National Laboratory (ORNL)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1096972
• Country of Publication: United States
• Language: English

Related Subjects

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pattern search