ZettaVox: Content Mining and Analysis Across Heterogeneous Compute Clouds__HadoopSummit2010
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Hadoop Summit 2010 - Application Track ZettaVox: Content Mining and Analysis Across Heterogeneous Compute Clouds Mark Davis, Kitenga
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ZettaVox: Content Mining and Analysis Across Heterogeneous Compute Clouds__HadoopSummit2010
- 4. Support Prediction Logic, Inc.
- 5. The Never-Ending Problem Multimedia Data Video Imagery Audio Sensor Streams Biometric data 3D Text Email Web pages Tweets Posts Enterprise Data Enterprise data CDRs Financial records Access logs
- 6. Solving the Problem is Hard Content mining analysts Machine learning specialists Information retrieval specialists Software Engineers Expensive and hard to find Parallel Supercomputers Racked clusters Systems management Enterprise storage solutions Gigabit switches Power management Text analytics Ontologies Database reporting tools ETL tools Business intelligence Open source components
- 10. Heterogenous Compute Clouds 42 U ≈ 84-168 cores 2 PCIe slots 15 multiprocessors 480 cores $0.13-$0.35/Gflop Amazon AWS Rackspace Mosso Private Cloud
- 14. Use and Visualize Results
- 15. ZettaVox Current Approach Slow analytics Methods don’t scale Expensive hardware Expensive software Capital investment Expertise investment Hadoop with GPU support Scalable SaaS Out-of-the-box expertise Rich user experience ZettaVox Internet-scale cloud and cluster-based content mining
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