Automatic Identification of Bottleneck Tasks for Business Process Management using Fusion-based Text Clustering

Abstract

Abstract With the arrival of the industrial big data era, it offers unprecedented opportunities for machine learning to intelligently uncover hidden tasks and restore the entire underlying process for business process modelling. While recent studies, e.g., process mining and ontologies, have advanced the research agenda of business process modelling and management, identifying a bottleneck task automatically needs more in-depth research. In this paper, a text mining-based bottleneck task identification approach is proposed. Firstly, to extract tasks from documents in different lengths, a dynamic sliding window is introduced to the biterm topic model. The sliding window size is adjusted according to document length during biterm selection process to ensure the two words in biterm comes from a context. Secondly, a fusion-based clustering algorithm is studied to uncover business tasks. The improved biterm topic model and the Doc2vec model are used to train two document vectors and then calculate two distances. The linear fusion of these two distances is used as the metric of clustering. Thirdly, the temporal frequency of each task at different periods is calculated to show the timeline and abnormal occurrence of tasks to identify bottleneck tasks. The proposed approach is evaluated using a data set containing the execution of a multi-year multidisciplinary student design project. The experiment results show the approach can effectively identify bottleneck tasks without manual intervention.