Using sampling theory as the basis for a conceptual data model

Abstract

Greater demands on forest resources require that larger amounts of information be readily available to decisionmakers. To provide more information faster, databases must be developed that are more comprehensive and easier to use. Data modeling is a process for building more complete and flexible databases by emphasizing fundamental relationships over existing or traditional business operations. Data modeling uses a hierarchical series of models beginning with a conceptual model of the activity of interest. From the conceptual model, a logical model is derived that captures more detail, but in an implementation-independent way. Finally, the logical model is transformed into a physical data model by means of application software. We show how sampling theory was used in a conceptual model to provide an integrating framework for identifying fundamental relationships. By using sampling theory, the final data structure organizes forest vegetation data gathering as a scientific process, rather than as specific business functions.

Parent Publication

• Integrated tools for natural resources inventories in the 21st century

Citation

Martin, Fred C.; Baggett, Tonya; Wolfe, Tom; Mita, Roy. 2000. Using sampling theory as the basis for a conceptual data model. In: Hansen, Mark; Burk, Tom, eds. Integrated tools for natural resources inventories in the 21st century. Gen. Tech. Rep. NC-212. St. Paul, MN: U.S. Dept. of Agriculture, Forest Service, North Central Forest Experiment Station. 464-470.