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Parallel Analysis of Egg Data with hadoop on futuregrid

Parallel Analysis of Egg Data with hadoop on futuregrid. Project Member: Rewati Ovalekar Project Guide : Gregor von Laszweski , Lizhe Wang. BACKGROUND. Importance of EEG Data: Used in detecting and diagnosing brain related dis-orders

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Parallel Analysis of Egg Data with hadoop on futuregrid

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  1. Parallel Analysis of Egg Data with hadoop on futuregrid Project Member: RewatiOvalekar Project Guide : Gregor von Laszweski, Lizhe Wang

  2. BACKGROUND Importance of EEG Data: • Used in detectingand diagnosing brain related dis-orders • EEMD algorithm is developed to analyze the signals

  3. Drawbacks of EEG Data: • EEG signals are complex in nature • Analysis of EEG signals are highly data-intensive and compute- intensive • Basic EEMD algorithm not time-efficient

  4. Parallel eemd for eeg analysis • EEMD algorithm was modified to analyze data points in parallel • Multiple levels: • Epoch Level • Trial Level • Data Channel Level

  5. Epoch Level: Single data point is considered and is processed at each level. The output from this instance is not consumed by another.

  6. Trial Level Each Epoch can be split into number of trials. Decomposition of each trail is performed independently. All trials for a particular epoch are combined to get an output for each epoch.

  7. Data Channel Level Data is parallelized at each channel, then the output is combined for its corresponding trial. The grain of parallelization is coarse at this level.

  8. Multi-thread design • Each thread will process eeg data point for a particular Epoch –level. • Local extrema will be calculated at each level. • All local maxima and minima will be connected using cubic spline

  9. Multi-thread design

  10. Limitations of multi-threaded design • Cannot process huge data due to availability of limited resources on a local machine Solution: • Develop Parallel EEMD algorithm using MapReduce on Hadoop

  11. Why Hadoop? Hadoop provides a distributed framework to run applications on large cluster • MapReduce is used to implement the parallel EEMD algorithm

  12. Mapreduce design: (epoch level parallelization) • Epoch Mapper: • Each map function will take input as single point • Calculate local extrema at each epoch level • Connect minima and maxima by cubic spline • Generate points which will be combined in Epoch Reducer

  13. Mapreduce design: (epoch level parallelization) • Epoch Reducer: • Each reduce function will combine the points having the same egg data point • Generates data points, 8 IMF and one left data for an individual eeg data point

  14. Mapreduce design: (epoch level parallelization)

  15. Code Available: /trunk/project/spring2011/EEMDAnalysis/EEMDJavain http://code.google.com/p/cyberaide/ • Tutorial is available to run the code on Hadoop

  16. Thank you!!!!

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