Large Process Model Repositories Research Articles

Efficiently querying large process model repositories in smart city cloud workflow systems based on quantitative ordering relations

With the development of cloud computing and the rise of smart city, smart city cloud service platforms are widely accepted by more and more enterprises and individuals. The underlying cloud workflow systems accumulate large numbers of business process models. How to achieve efficiently querying large process model repositories in smart city cloud workflow systems is challenging. To this end, this paper proposes an improved two-phase retrieval approach for querying large process model repositories in smart city cloud workflow systems. In the filtering stage, the index based on quantitative ordering relation with time and probability constraints (namely ORTP_index) is adopted to greatly reduce the number of candidate models in large process model repositories. In the refining phase, a process behavior similarity computing algorithm based on quantitative ordering relations is proposed to refine the candidate model set. Experiments illustrate that our proposal can significantly improve the query efficiency of large process model repositories in smart city cloud workflow systems based on behavior.

Efficient and exact query of large process model repositories in cloud workflow systems

As cloud computing platforms are widely accepted by more and more enterprises and individuals, the underlying cloud workflow systems accumulate large numbers of business process models. Retrieving and recommending the most similar process models according to the tenant's requirements become extremely important, for it is not only beneficial to promote the reuse of the existing model assets, but also helpful to reduce the error rate of the modeling process. Since the scales of cloud workflow repositories become bigger and bigger, developing efficient and exact query approaches is urgent. To this end, an improved two-stage exact query approach based on graph structure is proposed. In the filtering stage, the composite task index , which consists of the label, join-attribute and split-attribute of a task, is adopted to acquire candidate models, which can greatly reduce the number of process models needed to be tested by a time-consuming verification algorithm. In the verification stage, a novel subgraph isomorphism test based on task code is proposed to refine the candidate model set. Experiments are conducted on six synthetic model sets and two real model sets. The results demonstrate that the presented approach can significantly improve the query efficiency and reduce the query response time.

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.

Fast detection of exact clones in business process model repositories

As organizations reach higher levels of business process management maturity, they often find themselves maintaining very large process model repositories, representing valuable knowledge about their operations. A common practice within these repositories is to create new process models, or extend existing ones, by copying and merging fragments from other models. We contend that if these duplicate fragments, a.k.a. exact clones, can be identified and factored out as shared subprocesses, the repository's maintainability can be greatly improved. With this purpose in mind, we propose an indexing structure to support fast detection of clones in process model repositories. Moreover, we show how this index can be used to efficiently query a process model repository for fragments. This index, called RPSDAG, is based on a novel combination of a method for process model decomposition (namely the Refined Process Structure Tree), with established graph canonization and string matching techniques. We evaluated the RPSDAG with large process model repositories from industrial practice. The experiments show that a significant number of non-trivial clones can be efficiently found in such repositories, and that fragment queries can be handled efficiently.

Identifying refactoring opportunities in process model repositories

Context In order to ensure high quality of a process model repository, refactoring operations can be applied to correct anti-patterns, such as overlap of process models, inconsistent labeling of activities and overly complex models. However, if a process model collection is created and maintained by different people over a longer period of time, manual detection of such refactoring opportunities becomes difficult, simply due to the number of processes in the repository. Consequently, there is a need for techniques to detect refactoring opportunities automatically. Objective This paper proposes a technique for automatically detecting refactoring opportunities. Method We developed the technique based on metrics that can be used to measure the consistency of activity labels as well as the extent to which processes overlap and the type of overlap that they have. We evaluated it, by applying it to two large process model repositories. Results The evaluation shows that the technique can be used to pinpoint the approximate location of three types of refactoring opportunities with high precision and recall and of one type of refactoring opportunity with high recall, but low precision. Conclusion We conclude that the technique presented in this paper can be used in practice to automatically detect a number of anti-patterns that can be corrected by refactoring.