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structured data about a patient: data with controlled vocabulary (CV) property values and data with continuous property values (recorded/ captured more frequently) 6. Data consist of text and continuous numerical values significance, validate results, eliminate false positive/false negative results 5. Semantic graph mining algorithms to identify graph patterns, index and search graph 6. Semantic graph traversal Statistical Relational Artificial Intelligence for Health Care 1. Centralized data, with some data retrieved from Internet sources 2. Range from hundreds of gigabytes for a sample size to 1 PB for very large studies 3. Both constant updates/additions (to data subsets) and scheduled batch inputs 4. Large, longitudinal data 1. Relational probabilistic models/ probability theory; software that learns models from multiple data types and can possibly integrate the information and reason about complex queries 2. Robust and accurate learning methods to account for data imbalance (where large numbers of data are available 1. Java, some in house tools, [relational] database and NoSQL stores 2. Cloud and parallel computing 3. computer, 48 GB RAM (to perform analysis for a moderate sample size) 4. Dlusters for large datasets 5. 200 GB–1 TB hard drive for test 1. Visualization of very large data subsets 1. Secure handling and processing of data 1. Merging multiple tables before analysis 2. Methods to validate data to minimize errors --