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A Mobile Interactive System Generation Framework from Conceptual Model to Development Paradigm
Juanni Li,Qingyi Hua,Xiang Ji 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.3
The design of interactive systems is a process of transformation from problem domain to implementation domain. A mobile interactive system design differs from traditional software design in the perspective providing to conceptual modeling. With a diversity of user requirements and devices, it more concentrates on knowledge about the context of use rather than static information of problem domain. This paper proposes a multi-dimension description method which can describe those concepts related to special user requirements in the special context from four aspects: the set of static conceptual elements, process set, constraint set and interactive set. Then based on the conceptual model, a mobile interactive system generation framework is proposed to accurately map those concepts into requirement analysis and the implementation process; under the guidance of design pattern, a usable and useful interactive system can be achieved. Finally, we give a prototype of campus navigation system in which we successful used the generation framework.
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Wei Jia,Qingyi Hua,Minjun Zhang,Rui Chen,Xiang Ji,Bo Wang 한국정보처리학회 2019 Journal of information processing systems Vol.15 No.4
Mobile user interface pattern (MUIP) is a kind of structured representation of interaction design knowledge. Several studies have suggested that MUIPs are a proven solution for recurring mobile interface design problems. To facilitate MUIP selection, an effective clustering method is required to discover hidden knowledge of patterndata set. In this paper, we employ the semi-supervised kernel fuzzy c-means clustering (SSKFCM) method tocluster MUIP data. In order to improve the performance of clustering, clustering parameters are optimized byutilizing the global optimization capability of particle swarm optimization (PSO) algorithm. Since the PSOalgorithm is easily trapped in local optima, a novel PSO algorithm is presented in this paper. It combines animproved intuitionistic fuzzy entropy measure and a new population search strategy to enhance the populationsearch capability and accelerate the convergence speed. Experimental results show the effectiveness andsuperiority of the proposed clustering method.
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Jia, Wei,Hua, Qingyi,Zhang, Minjun,Chen, Rui,Ji, Xiang,Wang, Bo Korea Information Processing Society 2019 Journal of information processing systems Vol.15 No.4
Mobile user interface pattern (MUIP) is a kind of structured representation of interaction design knowledge. Several studies have suggested that MUIPs are a proven solution for recurring mobile interface design problems. To facilitate MUIP selection, an effective clustering method is required to discover hidden knowledge of pattern data set. In this paper, we employ the semi-supervised kernel fuzzy c-means clustering (SSKFCM) method to cluster MUIP data. In order to improve the performance of clustering, clustering parameters are optimized by utilizing the global optimization capability of particle swarm optimization (PSO) algorithm. Since the PSO algorithm is easily trapped in local optima, a novel PSO algorithm is presented in this paper. It combines an improved intuitionistic fuzzy entropy measure and a new population search strategy to enhance the population search capability and accelerate the convergence speed. Experimental results show the effectiveness and superiority of the proposed clustering method.
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