biological data 5. Metagenomic samples that can vary by several orders of magnitude, such as several hundred thousand genes to a billion genes 21 1. Distributed EHR 1. Data integration 1. data 1. Efficient 1. Protection of 1. Data annotated 1. Mobile data using ontological warehouse, open data health data in based on domain access Diabetes 2. Over 5 million annotation and source indexed accordance ontologies or patients with taxonomies Hbase visualization with privacy taxonomies thousands of 2. Parallel retrieval 2. needed policies and 2. Traceability of properties each algorithms for both supercomputers, legal data from origin and many more indexed and cloud and parallel requirements, (initial point of derived from custom searches; computing e.g., HIPAA. collection) through primary values identification of 3. I/O intensive 2. Security use 3. Each record: a data of interest; processing policies for 3. Data conversion range of 100 to patient cohorts, 4. HDFS storage different user from existing data 100,000 data patients’ meeting 5. custom code to roles warehouse into RDF property values, certain criteria, develop new triples average of 100 patients sharing properties from controlled similar stored data. vocabulary characteristics values, and 3. Distributed average of 1,000 graph mining continuous values algorithms, pattern 4. No real-time, analysis and graph but data updated indexing, pattern periodically; data searching on RDF timestamped with triple graphs the time of 4. Robust statistical observation (time analysis tools to
biological data 5. Metagenomic samples that can vary by several orders of magnitude, such as several hundred thousand genes to a billion genes 21 Diabetes 1. Distributed EHR data 2. Over 5 million patients with thousands of properties each and many more derived from primary values 3. Each record: a range of 100 to 100,000 data property values, average of 100 controlled vocabulary values, and average of 1,000 continuous values 4. No real-time, but data updated periodically; data timestamped with the time of observation (time 1. Data integration using ontological annotation and taxonomies 2. Parallel retrieval algorithms for both indexed and custom searches; identification of data of interest; patient cohorts, patients’ meeting certain criteria, patients sharing similar characteristics 3. Distributed graph mining algorithms, pattern analysis and graph indexing, pattern searching on RDF triple graphs 4. Robust statistical analysis tools to 1. data warehouse, open source indexed Hbase 2. supercomputers, cloud and parallel computing 3. I/O intensive processing 4. HDFS storage 5. custom code to develop new properties from stored data. 1. Efficient data visualization needed 1. Protection of health data in accordance with privacy policies and legal requirements, e.g., HIPAA. 2. Security policies for different user roles 1. Data annotated based on domain ontologies or taxonomies 2. Traceability of data from origin (initial point of collection) through use 3. Data conversion from existing data warehouse into RDF triples 1. Mobile access