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sources 2. Volume: > 12 million entities (patients), > 4 billion records or data points (discrete clinical observations), aggregate of > 20 TB raw data 3. Velocity: 500,000 to 1.5 million new transactions per day 4. Variety: formats include numeric, structured numeric, structured text, discrete nominal, discrete ordinal, discrete structured, binary large blobs (images and video) 5. Data evolve over time in a highly variable fashion sources and over time 2. Analytic techniques: information retrieval, NLP, machine learning decision models, maximum likelihood estimators, Bayesian networks 3. Teradata, PostgreSQL, MongoDB 4. Various, with significant I/O intensive processing consumers/ stakeholders, i.e., those who did not actually perform the analysis; specific visualization techniques to the results of analytics performed by informatics research scientists and health service researchers 2. Protection of all health data in compliance with governmental regulations 3. Protection of data in accordance with data providers, policies. 4. Security and privacy policies unique to a data subset 5. Robust security to prevent data breaches 2. Reduce errors and bias 3. Common nomenclature and classification of content across disparate challenging in the health IT space, as the taxonomies continue to evolve— SNOMED, International Classification of Diseases (ICD) 9 and future ICD 10, etc. 17 Pathology Imaging 1. High-resolution spatial digitized pathology images 1. image analysis to 1. Legacy system and cloud (computing 1. Visualization for validation 1. Security and privacy protection for 1. Human annotations for validation 1. 3D visualization and