Abstract
The Knowledge Provenance Management System, KProMS, can capture the complete provenance of the data, information, and knowledge of a structured activity by modeling the details of the associated data generation steps of that activity as workflows. Its unique workflow representation captures relationships between the processing steps, material and information flows, and data input and output. In this paper, we demonstrate the use of KProMS to manage and analyze the experimental data of an innovative system for manufacturing drug products using dropwise additive manufacturing. Dropwise additive manufacturing of pharmaceutical products (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 natures, and thus provide a rich illustration of KProMS capabilities to serve as knowledge management framework.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
