Request-driven generation of calculation chains for adaptive forest analysis

Abstract The aim of this study was to introduce a new method – request-driven generation of calculation (data and model) chains – to facilitate the automatic adaptation of analysis tools to varying output demands or input supply, and consequently reducing programming efforts. The method was implemented in a prototype for a calculation algorithm based on meta-information. To demonstrate the potential of the method, the algorithm was integrated with a model library and xml-based end-user interfaces for a case study where several calculation chains were generated for the comparison of different forest inventory systems. In our application, the autonomic analysis tool automatically adapted itself to varying output requests, input data sources, and contents of the model library. To summarize, the algorithm supports sharing and re-using of models and existing analysis tools. As a stand-alone calculation system, the algorithm can be utilized as a research and development tool, e.g. when testing and comparing models, calculation chains and cost-effective combinations of models, and data for practical forest inventory and planning systems. As an add-on or embedded component, the algorithm makes it easy to enhance data with varying models, or to adapt the existing analysis tools to locally accessible models or specific user requirements.