Browsing by Author "Fahmi, Faisal"

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Constructing a Service Software with Microservices
(2018-01-01)
Wang, Feng-Jian
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Fahmi, Faisal
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資訊工程學系
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電機資訊國際碩士學位學程
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Department of Computer Science
The microservice architecture is a variation of a Service-Oriented Architecture (SOA) that allows a service to be broken down into a number of smaller but independently concurrent rimming units so that both performance and maintainability of the application can get a big improvement. In this paper, we introduce an approach to constructing a software with layered and distributed microservices using object-oriented specifications. The proposed approach could increase the concurrency of the application system and thus improve the performance and could increase the reusability of microservices.
Improving the Detection o Sequential Anomalies Associated with a Loop
(2019-01-01)
Fahmi, Faisal
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Huang, Pei-Shu
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Wang, Feng-Jian
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資訊工程學系
;
電機資訊國際碩士學位學程
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.