Abstract: Systems and methods for predicting trace information include determining a plurality of trace candidates for one or more traces having missing path information, the plurality of trace candidates having path information for tasks of a business process model, which includes a plurality of independent parallel paths. Probabilities that each of the plurality of trace candidates for the business process model is an actual trace are computed using a processor for the one or more traces. One of the plurality of trace candidates is identified as the actual trace based on the probabilities to predict path information of the one or more traces.

Abstract: A method (and structure) for implementing a software tool, as executable by a processor on a computer to exercise any of a plurality of prediction tools. Questions are provided to a user output port, and inputs from a user input port are received as responses to the questions. The question responses are used to instantiate, customize, and configure a specific one of said plurality of prediction tools for executing a specific application on the software tool.

Abstract: A method for training a machine learning tool to generate a prediction in a business process includes receiving a business process model corresponding to the business process, the business process model including a plurality of tasks, identifying a cycling set at a decision point in the business process model, wherein the cycling set comprises at least one task that the business process model iterates through, and building a training table by determining a total number of sub-traces and a total number of variables from a plurality of execution traces of the business process model based on the cycling set identified at the decision point, wherein a new row of the training table is created for each of the sub-traces and a new column of the training table is created for each of the variables.

Abstract: A method for training a machine learning tool to generate a prediction in a business process includes receiving a business process model corresponding to the business process, the business process model including a plurality of tasks, identifying a cycling set at a decision point in the business process model, wherein the cycling set comprises at least one task that the business process model iterates through, and building a training table by determining a total number of sub-traces and a total number of variables from a plurality of execution traces of the business process model based on the cycling set identified at the decision point, wherein a new row of the training table is created for each of the sub-traces and a new column of the training table is created for each of the variables.

Abstract: A method for generating predictions includes dividing a business process model into fragments, wherein the business process model includes task nodes and at least one decision node, determining the decision node in at least one of the fragments, determining a decision tree for each decision node, determining a probability for reaching a terminal node in each fragment, and merging the probabilities obtained from the fragments to find a probability of a future task.