Abstract: The quality of predictive model outputs is improved by improving input data in the cases where entities in the input data are associated with one or more classes, computed at least in part from one or more subsets of the input data. Class association function characteristic data is derived from information describing a class association function that generates input data from source data. The class association function characteristic data comprises inferences relating to operation of the class association function that are not derivable solely from the input data. The input data is transformed into improved input data using a constructed class-specific transformation function and the class association function characteristic data.

Abstract: Values are attributed to components of a data analytics-driven system by representing the system as a computational graph. The computational graph embodies a function that takes one or more inputs and produces an output, and each component of the system is represented as a subgraph of the computational graph. A usage metric is calculated for each component of the system by determining whether the output of the function of the system is affected by the component. A utility metric is also calculated for each component of the system. Based on the calculated component usage metrics and utility metrics, respective value are allocated to the system components.