patient properties from stored data 22 Hundreds of GBs for a Constant updates Critical feature – data Mainly Java-based, Relational probabilistic Statistical single cohort of a few to EHRs; in other typically in multiple tables, tools to process models (Statistical Relational hundred people; controlled studies, need to be merged to the data Relational AI) learned Artificial possibly on the order data often in perform analysis from multiple data types Intelligence for of 1 PB when dealing batches at regular Health Care with millions of intervals patients 23 100 TB Low number of data Can be rich with various Charm++, MPI Simulations on a World feeding into the population activities, synthetic population Population-Scale simulation, massive geographical, Epidemiological amounts of cultural variations Study data generated by simulation 24 Tens of terabytes per During social unrest Big issues – data fusion, Specialized simulators, Models of behavior of Social Contagion year events, human combining data from open source software, humans and hard Modeling for interactions and different sources, dealing proprietary modeling infrastructures, models Planning mobility leads to with missing or incomplete environments; of their interactions, rapid changes in data databases visualization of results data; e.g., who follows whom in Twitter 25 N/A Real-time Rich variety and number of RDBMS Requires advanced and Biodiversity and processing and involved databases and rich visualization LifeWatch analysis in case of observation data natural or industrial disaster 26 Current datasets Much faster than Neural net very In-house GPU kernels Small degree of batch Large-Scale Deep typically 1 TB to 10 real-time heterogeneous as it learns and MPI-based statistical Learning TB, possibly 100 processing; for many different features communication all other million images to train autonomous developed by Stanford, data analysis performed a self-driving car driving, need to C++/Python source by the learning process thousands algorithm itself of high-resolution
patient properties from stored data 22 Statistical Relational Artificial Intelligence for Health Care Hundreds of GBs for a single cohort of a few hundred people; possibly on the order of 1 PB when dealing with millions of patients Constant updates to EHRs; in other controlled studies, data often in batches at regular intervals Critical feature – data typically in multiple tables, need to be merged to perform analysis Mainly Java-based, tools to process the data Relational probabilistic models (Statistical Relational AI) learned from multiple data types 23 World Population-Scale Epidemiological Study 100 TB Low number of data feeding into the simulation, massive amounts of data generated by simulation Can be rich with various population activities, geographical, cultural variations Charm++, MPI Simulations on a synthetic population 24 Social Contagion Modeling for Planning Tens of terabytes per year During social unrest events, human interactions and mobility leads to rapid changes in data; e.g., who follows whom in Twitter Big issues – data fusion, combining data from different sources, dealing with missing or incomplete data Specialized simulators, open source software, proprietary modeling environments; databases Models of behavior of humans and hard infrastructures, models of their interactions, visualization of results 25 Biodiversity and LifeWatch N/A Real-time processing and analysis in case of natural or industrial disaster Rich variety and number of involved databases and observation data RDBMS Requires advanced and rich visualization 26 Large-Scale Deep Learning Current datasets typically 1 TB to 10 TB, possibly 100 million images to train a self-driving car Much faster than real-time processing; for autonomous driving, need to process thousands of high-resolution Neural net very heterogeneous as it learns many different features In-house GPU kernels and MPI-based communication developed by Stanford, C++/Python source Small degree of batch statistical all other data analysis performed by the learning algorithm itself