Mission Print E-mail
Pragmatic Data LLC was formed in order to assist our clients with our unique expertise in the integration, standardization, conversion, aggregation, management, and analysis of complex data in the areas of science, healthcare, and general business.
Why Pragmatic? Print E-mail
Pragmatic Data means two things: firstly practical, i.e. our services are meant to solve a practical problem and when the problem is addressed, our clients and we are happy (all too often big software systems get in the way of the data.)  Secondly, our name means pragmatics-oriented, that is, pertaining to the purpose of your data for your business processes.
Healthcare Reference Information Model Print E-mail
Our founder, Dr. Schadow, has been chiefly designing the Health-Level-7 (HL7) standard Reference Information Model, an object-model that addresses of hundreds of complex use cases of biomedical data and workflow management.
Knowledge Management in SQL Print E-mail
We are maintaining medical concept dictionaries and ontologies in production grade relational database management systems (RDBMS.) In the past, RDBMS did not support transitive relational structures and had therefore been unsuitable for managing knowledge bases. The revised SQL-99 standard, however, may change this. In this paper we show that modern RDBMS that support recursive queries are capable of querying transitive relationships in a generic data model. We show a simple but efficient indexed representation of transitive closure. We could confirm that even challenging combined transitive relationships can be queried in SQL.
Our Expertise Print E-mail
We are highly experienced in managing biomedical data, both individual healthcare data as well as the knowledge on medical subject matter which is used to drive intelligent computer systems functions, such as decision support algorithms. Biomedical data is the most complex data and combines science and business data. To cope successfully with biomedical data, we have developed many scalable and reusable methods applicable for other sciences and business data and processes.

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