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Description
All information models have meaning, but sometimes they mean more to the analyst and the implementer than they do to the subject matter expert. This course will provide the tools for converting the requirements in an information model into simple sentences that are easily understood. The natural language analysis procedure then creates a series of questions that can be answered Yes or No by subject matter experts. The analyst can then answer the same questions by referencing the graphical information model. When answers are not consistent, a portion of the model must be reworked until the subject matter expert agrees with the defined rules.
During this three day course students will be trained to convert several types of graphical models into simple sentences. The structure of the required questions will be understood and students will develop skills in answering the questions from the point of view of the graphical model.
The course emphasis will be on helping experienced and inexperienced modelers better communicate their understanding of the requirements to users, subject matter experts and managers and provide a way for a subject matter expert to validate the contents of a graphical model. Using this approach, the subject matter expert is only required to know the desired or existing rules for their subject area and they are not required to learn about or understand the complexities that exist in graphical models.
Objectives
Students will:
- learn how to create simple natural language sentences from information models
- understand how to create questions that will define the rules for the sentences
- answer the questions based on the information model
- validate an information model based on subject matter experts' answers to the questions
Who Should Attend
- Technical and managerial professionals involved in creating or using information models
Instructor(s)
- John Sharp, NIH Office of the Chief IT Architect
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