AUTOMATICALLY COMPLETING A PIPELINE GRAPH IN AN INTERNET OF THINGS NETWORK

An approach is provided for completing a pipeline graph. Using a deep learning based sequence model, an initial data pipeline having a sequence of nodes is generated. Mismatch(es) between data formats required by input and output in the sequence of nodes is identified. Virtual gap node(s) that correct the mismatch(es) are added to the initial data pipeline. For a given virtual gap node, tentative graph structures are determined using knowledge graphs and a crowd sourced validation system. Reuse forecast scores and performance scores for the tentative graph structures are calculated. Based on the reuse forecast scores and the performance scores, a final graph structure for implementing the given virtual gap node is determined.