Member of the Helmholtz Association 03/10/2015 | RDA Fifth Plenary Meeting | San Diego, USA | Paradise Point Resort Markus Götz Jülich Supercomputing Center (JSC) // University of Iceland Morris Riedel Jülich Supercomputing Center (JSC) // University of Iceland Big Data Interest Group Smart Data Analytics

Member of the Helmholtz Association Outline Introduction  Research Group, Research Area Smart Data Analytics Use Cases and Techniques  Classification, Land Cover Type, piSVM  Clustering, „Drunken Flies“, HPDBSCAN  Deep-Learning, Cortex Layers, pylearn CNN Conclusion  Results and RDA Context 03/10/20152Markus Götz | Smart Data Analytics | Forschungszentrum Jülich

Member of the Helmholtz Association 03/10/20153 Research Group  Jülich Supercomputing Center (HPC/HTC)  High Productivity Data Processing Group Research Area  Smart Data Analytics Methods  Evaluation and Development of Scalable Tools  Processing Platform Requirements  Application in Scientific Use Case Introduction Markus Götz | Smart Data Analytics | Forschungszentrum Jülich Parallel Data Analytics Data Mining Methods Machine Learning Algorithms Scientific Community Application Data Analzsis Tools Generic Data Methods Smart Data Analytics

Member of the Helmholtz Association 03/10/20154 Land Cover Type Problem  Collaboration with University of Iceland  Determine Land Cover Type in Satellite Images  Different Types - Road, Building, Vegetation, … Classification  Supervised Learning Technique  Known Set of Groups or Classes  Determine Membership of New Items Classification // Land Cover Type Markus Götz | Smart Data Analytics | Forschungszentrum Jülich

Member of the Helmholtz Association 03/10/20155 Approach  Support Vector Machines (SVM)  Existing Solution: piSVM (MPI)  In-house Optimization of Parallel Code Classification // Land Cover Type Markus Götz | Smart Data Analytics | Forschungszentrum Jülich Area Standard deviation Inertia

Member of the Helmholtz Association 03/10/20156 „Drunken Flies“  Collaboration with University of Cologne  Investigate Influence of Genetics on Alcohol Consumption  Literally Make Flies Drunk Clustering  Unsupervised Learning Technique  Subdivide Database into Similar Groups  Similarity Metrics Clustering // „Drunken Flies“ Markus Götz | Smart Data Analytics | Forschungszentrum Jülich

Member of the Helmholtz Association 03/10/20157 Clustering // „Drunken Flies“ Markus Götz | Smart Data Analytics | Forschungszentrum Jülich Approach  Image Processing Pipeline  HPDBSCAN  In-house Development (MPI+OpenMP)

Member of the Helmholtz Association 03/10/20158 Cortex Layer Problem  Institute for Neuro-Medicine (INM) at FZJ  Segment the Seven Layers of the Cortex  Images of Actual Brain Slices  Each Gigabytes (60k square resolution) Deep Learning  Supervised Learning Technique (Classification)  More Advanced Mathemical Models  Various Flavors of Neural Networks Deep Learning // Cortex Layers Markus Götz | Smart Data Analytics | Forschungszentrum Jülich

Member of the Helmholtz Association 03/10/20159 Approach  Convolutional Neural Networks  Existing Serial Toolkit  Pylearn 2/Theano  Scaling Issues Deep Learning // Cortex Layers Markus Götz | Smart Data Analytics | Forschungszentrum Jülich

Member of the Helmholtz Association 03/10/ Conclusion Markus Götz | Smart Data Analytics | Forschungszentrum Jülich Results  Big Data Challenge is Real!  Gap between Analytics Requirements and Actual Implementations Interest for RDA  Code is GitHub and Bitbucket  Data is Open and Freely B2SHARE  Choice of Dataformats  Question of Future Processing Platforms

Member of the Helmholtz Association 03/10/ Thanks you for the attention… Markus Götz | Smart Data Analytics | Forschungszentrum Jülich Fifth Plenary Meeting 08 – 12 March 2015 San Diego, USA | Paradise Point Resort Contact: Big Data IG > Wiki > 5th Plenary