A Process Model Collection with Structural Variants and Evaluations

Today in the era of the latest technologies, Business Process Management Systems (BPMS) have allowed organizations to build process model repositories which help to maintain the flow of operations in the form of various process models. Business process models are virtual models that can imitate the actual activities of an organization. Searching for semantically similar activities between pairs of process models in a repository is known as Process Model Matching (PMM). From the past few years, PMM has been gaining momentum due to its wide range of applications such as integration of process models, process model clone detection, and process model knowledge discovery. Different types of PMM techniques have been applied on available process model repositories but these repositories contained a limited number of process models. Another notable aspect of PMM is that the existing techniques have not achieved the desired results which questions the effectiveness of process model repositories. To address this problem, the authors of this study have developed a substantial, diverse, and carefully developed process model collection. This process model collection is compared with existing SAP collection to highlight its significance and superiority. Furthermore, the proposed process model collection represents structural variations of example process models which are governed by the defined set of rules. To reflect structural variations between process models of our collection, existing structural similarity approaches such as structural metrics and graph edit distance were applied by using a custom-developed tool. Our proposed process model collection is freely available to the research community which can be used to build new PMM techniques and for assessment of existing PMM techniques.

@article{Anam2020,
  title     = {A Process Model Collection with Structural Variants and Evaluations},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {5},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Rimsha Anam and Tahir Muhammad},
  doi       = {10.14569/IJACSA.2020.0110565},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110565}
}