  Through purposeful introduction of malicious transactions (tracking
transactions) into randomly select nodes of a (database) graph, soiled and
clean segments are identified. Soiled and clean measures corresponding those
segments are then computed. These measures are used to repose the problem of
critical database elements detection as an optimization problem over the graph.
This method is universally applicable over a large class of graphs (including
directed, weighted, disconnected, cyclic) that occur in several contexts of
databases. A generalization argument is presented which extends the critical
data problem to abstract settings.
