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Data Analysis and High Performance Computing

Data Analysis and High Performance Computing. Yu (Cathy) Jiao, Ph.D. Robert M. Patton, Ph.D. Xiaohui Cui, Ph.D. Applied Software Engineering Research Group Computational Sciences and Engineering Division. Streaming data analysis challenges. High volume data streams are constantly generated.

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Data Analysis and High Performance Computing

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  1. Data Analysis and High Performance Computing Yu (Cathy) Jiao, Ph.D. Robert M. Patton, Ph.D. Xiaohui Cui, Ph.D. Applied Software Engineering Research Group Computational Sciences and Engineering Division

  2. Streaming data analysis challenges • High volume data streams are constantly generated. • Traditional data encoding scheme is inefficient. • Need a new solution to handle incremental clustering. Sensors Multimedia ? Image Data Text Binary One small step for man 11010010 1970 1980 1990 2000 2010

  3. Distributed data stream mining with Piranha • Piranha utilizes distributed and parallel data clustering to process data streams. • Piranha applies a novel data encoding scheme, Term Frequency-Inverse Corpus Frequency (TF-ICF). • Piranha handles incremental clustering using a threshold-based solution.

  4. headnode worknode worknode worknode worknode

  5. Solar Activity Modeling the impact of policies Policy: Budget cut for public transportation Effect: Gas price soars to $6.00 per gallon Population Change Volcano Activity Oil Production Car Emission Regional Conflicts Global Cooling Abnormal Weather Cloud Feedback Ocean Activity Disappearing Glacier Global Warming New Diseases Climate Change Endangered Ecosystem Drought Industrial Pollution Rainfall Causes Effects

  6. Breakthrough—bioinspired distributed solution Ant colony optimization Bird flocking model Alignment Separation Cohesion

  7. Multiple species flocking (MSF) document clustering Alignment Separation Cohesion The clustering results of K-means, ant clustering and MSF clustering algorithm on synthetic and document datasets after 300 iterations The document collection dataset

  8. Summary • Current technology cannot solve emerging national challenges. • Intelligent software agents are a significant breakthrough technology. • Results indicate high potential to help solve these national challenges. • We have a progression of successfully deployed agent systems and research to our credit.

  9. Contacts Yu (Cathy) Jiao, Ph.D. Applied Software Engineering Research Group Computational Sciences and Engineering Division (865) 574-0647 jiaoy@ornl.gov Robert M. Patton, Ph.D. Applied Software Engineering Research Group Computational Sciences and Engineering Division (865) 576-3832 pattonrm@ornl.gov Xiaohui Cui, Ph.D. Applied Software Engineering Research Group Computational Sciences and Engineering Division (865) 576-9654 cuix@ornl.gov 12 Jiao_Knowledge_SC07

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