An Approach to Tracking Data Derivation in Information Systems

Modern enterprises are becoming increasingly dependent upon the data produced from information systems to support their operations. So the knowledge of how such data are derived is vitally important to them. Unfortunately, as the systems evolve, their functionality tends to become more complex and associated documentation less reliable. Consequently, accurate knowledge of data derivation cannot always be assumed. In this paper, we propose an approach to tracking this valuable knowledge automatically by analysing the information systems themselves. Our approach is centred around two key technologies: symbolic execution for abstracting data derivation and query inversion for determining data sources, and allows the user to track data derivation as and when it is needed