| Title | Detecting Data-model-oriented Anomalies in Parallel Business Process |
| Authors | Yin, Ning Wang, Shanshan Li, Hongyan Fan, Lilue |
| Affiliation | Peking Univ, Key Lab Machine Percept, Minist Educ, Beijing 100871, Peoples R China. Peking Univ, Sch Elect Engn & Comp Sci, Beijing 100871, Peoples R China. Peking Univ, Key Lab Machine Percept, Minist Educ, Beijing 100871, Peoples R China. Li, HY (reprint author), Peking Univ, Sch Elect Engn & Comp Sci, Beijing 100871, Peoples R China. |
| Keywords | Parallel business process Data model Data-model-oriented anomalies Anomalies detection Semantic verification SUPPORT |
| Issue Date | 2016 |
| Publisher | 17th International Conference on Web-Age Information Management (WAIM) |
| Citation | 17th International Conference on Web-Age Information Management (WAIM).2016,9659,65-77. |
| Abstract | Currently, most information systems are data intensive. The data models of such are posing notable influence on business processes. However, the predominance of existed process verification methods leave out the impact of data models on process models. Meanwhile, with parallel structures in business processes multiplying, business process structures are becoming increasingly intricate and large in size. A parallel structure engenders also uncertainty, and consequently increases the chances and decreases the detectability of anomalies occasioned by process and data model conflicts. In this paper, these anomalies are analyzed and classified. A data state matrix and data operation algebra is introduced to establish the relation between the parallel-process model and the data model. Then, an anomaly detection method under the divide-and-conquer framework is proposed to ensure efficiency in detecting anomalies in business processes. Both theoretical analysis and experimental results prove this method to be highly efficient and effective in detecting data model oriented anomalies. |
| URI | http://hdl.handle.net/20.500.11897/449574 |
| ISSN | 0302-9743 |
| DOI | 10.1007/978-3-319-39958-4_6 |
| Indexed | EI CPCI-S(ISTP) |
| Appears in Collections: | 机器感知与智能教育部重点实验室 信息科学技术学院 |
