semantic graph-mining algorithms to identify graph patterns, index graph, and search graph; indexed Hbase; custom code to develop new patient properties from stored data 22 Hundreds of Constant updates Critical feature – data Mainly Java-based, Relational probabilistic Statistical gigabytes for a single to EHRs; in other typically in multiple tables, tools to process models (Statistical Relational cohort of a few controlled studies, need to be merged to the data Relational AI) learned from Artificial hundred people; data often in perform analysis multiple data types Intelligence for possibly on the order batches at regular Health Care of 1 PB when dealing intervals with millions of patients 23 100 TB Low number of Can be rich with various Charm++, MPI Simulations on a synthetic World data feeding into population activities, population Population-Scale the simulation, geographical, Epidemiological massive amounts cultural variations Study of real-time data generated by simulation 24 Tens of terabytes per During social Big issues – data fusion, Specialized simulators, Models of behavior of Social Contagion year unrest events, combining data from open source software, humans and hard Modeling for human different sources, dealing proprietary modeling infrastructures, models of Planning interactions and with missing or incomplete environments; their interactions, mobility leads to data databases visualization of results rapid changes in 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
semantic graph-mining algorithms to identify graph patterns, index graph, and search graph; indexed Hbase; custom code to develop new patient properties from stored data 22 Statistical Relational Artificial Intelligence for Health Care Hundreds of gigabytes 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 real-time 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 Rich variety and number of involved databases and observation data RDBMS Requires advanced and rich visualization