The scientist’s investigation covers issues in Data mining, Metadata, Inference, USable and Correctness. James R. Kraemer has included themes like Event, Chaotic, Set, Constraint and Cohort in his Data mining study. James R. Kraemer combines subjects such as Digital video, Real-time computing, World Wide Web and Customer identification with his study of Set.
His Cohort research incorporates themes from Computer program and Optimal control. His work in Metadata tackles topics such as Object which are related to areas like Pattern recognition and Video based. James R. Kraemer has researched Correctness in several fields, including Key, Information retrieval, Reliability and Database.
James R. Kraemer spends much of his time researching Data mining, Computer program, Set, Database and Cohort. His research integrates issues of Event, Metadata, Inference and Information retrieval in his study of Data mining. His Metadata repository study in the realm of Metadata interacts with subjects such as USable.
His Inference course of study focuses on Correctness and Reliability. His Computer program research includes themes of Object, Event, Context based and Product. His work investigates the relationship between Set and topics such as Artificial intelligence that intersect with problems in Pattern recognition.
His primary areas of study are Computer program, Computer network, Set, Computer security and Protocol. His Computer program research includes elements of Process, Virtual machine, Software and Resource. His Computer network research is multidisciplinary, incorporating perspectives in Database transaction and Distributed computing.
His studies in Set integrate themes in fields like Data mining, Event, Cognition, Event and Computation. His Data mining research focuses on subjects like Product, which are linked to Object. In his research, Metadata is intimately related to Digital sensors, which falls under the overarching field of Event.
His main research concerns Computer program, Computer network, Object, Set and Distributed computing. His Computer program research is multidisciplinary, incorporating elements of Payload, Chip and Exclusive or. The various areas that James R. Kraemer examines in his Computer network study include Decentralized computing and Download.
His Object-oriented design, Method and Object model study, which is part of a larger body of work in Object, is frequently linked to USable, bridging the gap between disciplines. His Object-oriented design study combines topics in areas such as Data mining, Context model and Product. In his study, James R. Kraemer carries out multidisciplinary Set and Product research.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
System and method for a multiple disciplinary normalization of source for metadata integration with ETL processing layer of complex data across multiple claim engine sources in support of the creation of universal/enterprise healthcare claims record
Robert R. Friedlander;Richard A. Hennessy;James R. Kraemer.
(2007)
Inference of anomalous behavior of members of cohorts and associate actors related to the anomalous behavior
Robert R. Friedlander;James R. Kraemer.
(2008)
System and method for deriving a hierarchical event based database having action triggers based on inferred probabilities
Robert R. Friedlander;Richard A. Hennessy;James R. Kraemer.
(2007)
System and method for deriving a hierarchical event based database having action triggers based on inferred probabilities
Robert R. Friedlander;Richard A. Hennessy;James R. Kraemer.
(2007)
System and method for deriving a hierarchical event based database optimized for analysis of biological systems
Robert R. Friedlander;Richard A. Hennessy;James R. Kraemer.
(2007)
System and method to optimize control cohorts using clustering algorithms
Robert R. Friedlander;Richard A. Hennessy;James R. Kraemer;John Baxter Rollins.
(2006)
Generating Generalized Risk Cohorts
Robert Lee Angell;Robert R. Friedlander;James R. Kraemer.
(2008)
Automatic generation of new rules for processing synthetic events using computer-based learning processes
Robert Lee Angell;Robert R. Friedlander;James R. Kraemer.
(2008)
System and method for deriving a hierarchical event based database optimized for pharmaceutical analysis
Robert R. Friedlander;Richard A. Hennessy;James R. Kraemer.
(2007)
System and method for deriving a hierarchical event based database optimized for clinical applications
Robert R. Friedlander;Richard A. Hennessy;James R. Kraemer.
(2007)
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