Abstract: A substantial learning curve is required to construct integration processes in an integration platform. This can make it difficult for novice users to construct effective integration processes, and for expert users to construct integration processes quickly and efficiently. Accordingly, embodiments for building and operating a model to predict next steps, during construction of an integration process via a graphical user interface, are disclosed. The model may comprise a Markov chain, prediction tree, or an artificial neural network (e.g., graph neural network, recurrent neural network, etc.) or other machine-learning model that predicts a next step based on a current sequence of steps. In addition, the graphical user interface may display the suggested next steps according to a priority (e.g., defined by confidence values associated with each step).

Abstract: A substantial learning curve is required to construct integration processes in an integration platform. This can make it difficult for novice users to construct effective integration processes, and for expert users to construct integration processes quickly and efficiently. Accordingly, embodiments for building and operating a model to predict next steps, during construction of an integration process via a graphical user interface, are disclosed. The model may comprise a Markov chain, prediction tree, or an artificial neural network (e.g., graph neural network, recurrent neural network, etc.) or other machine-learning model that predicts a next step based on a current sequence of steps. In addition, the graphical user interface may display the suggested next steps according to a priority (e.g., defined by confidence values associated with each step).