Efficient querying of large process model repositories

Recent years have seen an increased uptake of business process management technology in industries. This has resulted in organizations trying to manage large collections of business process models. One of the challenges facing these organizations concerns the retrieval of models from large business process model repositories. For example, in some cases new process models may be derived from existing models, thus finding these models and adapting them may be more effective and less error-prone than developing them from scratch. Since process model repositories may be large, query evaluation may be time consuming. Hence, we investigate the use of indexes to speed up this evaluation process. To make our approach more applicable, we consider the semantic similarity between labels. Experiments are conducted to demonstrate that our approach is efficient.

► To improve business process model query efficiency, we use indexing technologies.
► To increase the accuracy of query results, we consider data and resource aspects in addition to control-flow aspects.
► To make our approach more applicable, we consider a notion of semantic similarity between labels.
► To evaluate our approach, we implement it in BeehiveZ system.