drop in cost of interactive hosts levels of sequencing 5. sequencing resolution. technology. and comparative Ability to view 4. heterogeneous, analysis abstract complex, techniques for representations structural, and highly complex of highly similar hierarchical data data. biological data. 6. descriptive 5. metagenomic statistics samples can vary by several orders of magnitude, such as several hundred thousand genes to a billion genes 1. distributed 1. data 1. data 1. efficient 1. protection of 1. data annotated 1. mobile access EHR data integration, using warehouse, open based health data in based on domain 2. over 5 million ontological source indexed visualization is accordance with ontologies or patients with annotation and Hbase needed legal taxonomies. Diabetes thousands of taxonomies 2. requirements - 2. to ensure properties each 2. parallel supercomputers, eg, HIPAA - and traceability of and many more retrieval cloud and privacy policies. data, from origin that are derived algorithms for parallel 2. security [initial point of from primary both indexed and computing policies for collection] values. custom searches, 3. I/O intensive different user through to use. 3. each record, a identify data of processing roles. 3. convert data range of 100 to interest. Patient 4. HDFS storage from existing 100,000 data cohorts, patients 5. custom code to data warehouse property values meeting certain develop new into RDF triples average of 100 criteria, patients properties from controlled sharing similar stored data. vocabulary characteristics values and 1000 3. distributed continuous graph mining values. algorithms, 21 4. none real-time pattern analysis but data updated and graph periodically. indexing, pattern Data is searching on timestamped RDF triple with the time of graphs observation [time 4. robust that the value is statistical recorded.] analysis tools to 5. structured data manage false about a patient discovery rate, falls into two determine true main categories: subgraph data with significance, controlled validate results, vocabulary [CV] eliminate false property values, positive / false and continuous negative results property values 5. semantic graph [which are mining recorded / algorithms to
drop in cost of sequencing technology. 4. heterogeneous, complex, structural, and hierarchical biological data. 5. metagenomic samples can vary by several orders of magnitude, such as several hundred thousand genes to a billion genes interactive hosts 5. sequencing and comparative analysis techniques for highly complex data 6. descriptive statistics levels of resolution. Ability to view abstract representations of highly similar data. 21 Diabetes 1. distributed EHR data 2. over 5 million patients with thousands of properties each and many more that are derived from primary values. 3. each record, a range of 100 to 100,000 data property values average of 100 controlled vocabulary values and 1000 continuous values. 4. none real-time but data updated periodically. Data is timestamped with the time of observation [time that the value is recorded.] 5. structured data about a patient falls into two main categories: data with controlled vocabulary [CV] property values, and continuous property values [which are recorded / 1. data integration, using ontological annotation and taxonomies 2. parallel retrieval algorithms for both indexed and custom searches, identify 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 manage false discovery rate, determine true subgraph significance, validate results, eliminate false positive / false negative results 5. semantic graph mining algorithms 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 based visualization is needed 1. protection of health data in accordance with legal requirements - eg, HIPAA - and privacy policies. 2. security policies for different user roles. 1. data annotated based on domain ontologies or taxonomies. 2. to ensure traceability of data, from origin [initial point of collection] through to use. 3. convert data from existing data warehouse into RDF triples 1. mobile access