標題: | Improving the Detection o Sequential Anomalies Associated with a Loop |
作者: | Fahmi, Faisal Huang, Pei-Shu Wang, Feng-Jian 資訊工程學系 電機資訊國際碩士學位學程 Department of Computer Science EECS International Graduate Program-Master |
關鍵字: | artifact anomaly;workflow;redundant computation |
公開日期: | 1-一月-2019 |
摘要: | Workflow models are widely applied in business software design. A workflow model contains a set of systematic ordered tasks to achieve designated business goal(s) under the designed flow control. Analyzing artifact usage during design phase can prevent unexpected artifact result due to abnormal artifact operation(s). A sequential anomaly indicates a pair of activities operating on the same artifact that can result in redundant write or missing production. On the other hand, the iteration of a loop structure in a workflow cannot be statically analyzed, thus, detecting process of artifact anomalies in a loop is costly. In this paper, we present an effective method to detect all anomalies associated with a loop by removing the redundant computation due to the repeated structure of the body and control in the iterations. After the removing, the anomalies can be detected on a single iteration generated instead. Here, the process of anomaly detection is now simplified into two phases: First, a workflow model is transformed into a corresponding C-tree structure and next, the proposed anomaly detection methodology is applied to the C-tree. Compared with current approaches, our method can reduce the space complexity and decrease the execution times of anomaly detection as linear. |
URI: | http://dx.doi.org/10.1109/COMPSAC.2019.10195 http://hdl.handle.net/11536/154478 |
ISBN: | 978-1-7281-2607-4 |
ISSN: | 0730-3157 |
DOI: | 10.1109/COMPSAC.2019.10195 |
期刊: | 2019 IEEE 43RD ANNUAL COMPUTER SOFTWARE AND APPLICATIONS CONFERENCE (COMPSAC), VOL 2 |
起始頁: | 127 |
結束頁: | 134 |
顯示於類別: | 會議論文 |