Abstract: Systems and methods for predicting feature values in a matrix are disclosed. In example embodiments, a server accesses a matrix, the matrix having multiple dimensions, one dimension of the matrix representing features, and one dimension of the matrix representing entities. The server separates the matrix into multiple submatrices along a first dimension, each submatrix including all cells in the matrix for a set of values in the first dimension. The server provides the multiple submatrices to multiple machines. The server computes, using each machine, a correlation between values in at least one second dimension of the matrix and a value for a preselected feature in the matrix, the correlation being used to predict the value for the preselected feature based on other values along the at least one second dimension. The server provides an output representing the computed correlation.

Abstract: Systems and methods for deduplication and disambiguation are disclosed. In example embodiments, a server accesses stored information about a first entity and stored information about a second entity. The server determines, based on the accessed stored information about the first entity and the accessed stored information about the second entity, a set of information items known about both the first entity and the second entity. The server computes, based on the set of information items, a probability that the first entity corresponds to the second entity by computing one or more expressiveness scores corresponding to a value of a first information item and a value of a second information item from the set of information items. The server provides, as a digital transmission, an output representing the computed probability.