4. Large, account for data 4. Dlusters for imbalance (where large datasets longitudinal data large numbers of 5. 200 GB–1 TB 5. Rich relational data are available hard drive for test data comprising for a small number data multiple tables, of subjects) different data 3. Learning types such as algorithms to imaging, EHR, identify skews in demographic, data, so as to not genetic, and to (incorrectly) natural language model noise data requiring 4. Generalized and rich refined learned representation models for 6. Unpredictable application to arrival rates, diverse sets of data often real time 5. Challenge: acceptance of data in different modalities (and from disparate sources) 23 1. File-based 1. 1. Movement of 1. 1. Protection of 1. Data quality, -- World synthetic and very large volume Visualization PII on ability to capture the Population population, either of data for individuals used traceability of Scale centralized or computation, like visualization in modeling quality from distributed sites supercomputer (networking) 2. Data computation Study 2. Large volume performance 2. Distributed protection and of real-time 2. Unstructured MPI-based secure platform output data and irregular simulation system for 3. Variety of nature of graph (platform) computation output datasets processing 3. Charm++ on depending on the 3. Summary of multi-nodes model’s various runs of (software)
4. Large, longitudinal data 5. Rich relational data comprising multiple tables, different data types such as imaging, EHR, demographic, genetic, and natural language data requiring rich representation 6. Unpredictable arrival rates, often real time account for data imbalance (where large numbers of data are available for a small number of subjects) 3. Learning algorithms to identify skews in data, so as to not to (incorrectly) model noise 4. Generalized and refined learned models for application to diverse sets of data 5. Challenge: acceptance of data in different modalities (and from disparate sources) 4. Dlusters for large datasets 5. 200 GB–1 TB hard drive for test data 23 World Population Scale Study 1. File-based synthetic population, either centralized or distributed sites 2. Large volume of real-time output data 3. Variety of output datasets depending on the model’s 1. and computation, like supercomputer performance 2. Unstructured and irregular nature of graph processing 3. Summary of various runs of 1. Movement of very large volume of data for visualization (networking) 2. Distributed MPI-based simulation system (platform) 3. Charm++ on multi-nodes (software) 1. Visualization 1. Protection of PII on individuals used in modeling 2. Data protection and secure platform for computation 1. Data quality, ability to capture the traceability of quality from computation --