Abstract
Abstract The Knowledge Provenance Management System, KProMS captures the complete provenance of knowledge of a structured activity by modeling the details of the associated knowledge generation steps of that activity as workflows. Its unique workflow representation captures relationships between the processing steps and material and information flows, and data input and output. In this paper, we demonstrate the use of KProMS to manage and analyze experimental data for an innovative test bed for manufacturing drug products using drop-wise additive manufacturing. The drop-wise additive manufacturing system (DAMPP) uses drop-on-demand printing technology for depositing various drug formulations onto edible substrates. DAMPP requires and generates a range of data types, including camera and IR images, spectra and numerical parameter values, both of real time and off-line nature and thus serves as rich illustration of KProMS capabilities to serve as knowledge management framework.
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