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-Jan-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
Appears in Collections:Conferences Paper