Name
Abstract | ||
|---|---|---|
With the broad use of business process management technology, there are more and more business process models. Since the ability of different modelers is different, the quality of these models varies. A question arises here is that, can we refactor these models to improve the quality as practised in software engineering? Business process modeling can be regarded as declarative programming, and business process models can be used to drive the process aware information systems, which are generally developed with model driven architecture, so business process models are crucial for the efficiency of process aware information systems. In this paper, we propose a novel approach on how to systematically refactor business process models with parallel structures for sequence structures for the first time. More specifically, we analyze the real causal relations between business tasks based on data operation dependency analysis, and refactor business process models with process mining technology. After comprehensive model refactoring, parallel execution of business tasks can be maximized, so the efficiency of business processing can be improved, that is, the quality of business process models can be improved. Analysis and experiments show that our approach is effective and efficient. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TSC.2014.2383391 | IEEE Trans. Services Computing |
Keywords | Field | DocType |
business process,model,parallel,refactor | Artifact-centric business process model,Business process management,Business process,Software engineering,Computer science,Process modeling,Business process modeling,Business process discovery,Business rule,Business Process Model and Notation,Distributed computing,Process management | Journal |
Volume | Issue | ISSN |
PP | 99 | 1939-1374 |
Citations | PageRank | References |
2 | 0.36 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Jin | 1 | 69 | 5.71 |
| Jianmin Wang | 2 | 2446 | 156.05 |
| Yun Yang | 3 | 2103 | 150.49 |
| Lijie Wen | 4 | 452 | 44.34 |