Abstract: A method and apparatus for detecting an error in a business process via an exchange of email messages. In one example, the method may be executed by a processor of a business process analysis server (BPAS). For example, the method includes receiving an email, wherein the email includes an address of the BPAS, analyzing the email to determine at least one feature, determining the business process based on the at least one feature, determining one or more variables that is associated with the business process, detecting the error in the business process associated with the email based on at least variable of the one or more variables associated with the business process and generating an alert email in response to the error that is detected, wherein the alert email requests a correction to the at least one variable to complete the business process.

Abstract: A method and apparatus for detecting an error in a business process via an exchange of email messages. In one example, the method may be executed by a processor of a business process analysis server (BPAS). For example, the method includes receiving an email, wherein the email includes an address of the BPAS, analyzing the email to determine at least one feature, determining the business process based on the at least one feature, determining one or more variables that is associated with the business process, detecting the error in the business process associated with the email based on at least variable of the one or more variables associated with the business process and generating an alert email in response to the error that is detected, wherein the alert email requests a correction to the at least one variable to complete the business process.

Abstract: A method and system for transforming a multivariate data domain into a low-dimensional visual representation. Probabilistic models of the data domain are utilized, and at least one probabilistic model is used to produce at least one predictive distribution. The predictive distributions are used as inputs to the visualization process, where the multidimensional space is converted to a low-dimensional space. In this process data vectors are considered similar, for example, if the corresponding instances of a predictive distribution, conditioned with the variable value assignments found in the data vectors, are similar. Consequently, similarity is not defined directly using the physical properties of the data vectors, but indirectly through the probabilistic predictive model(s).