10 research outputs found

    Interactive visual data query & exploration : techniques for visual data analytics through visual query modelling and multidimensional data interaction

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    University of Technology Sydney. Faculty of Engineering and Information Technology.The direct data manipulation through visualization and associated navigation techniques has been implemented for many years. However, these methods are not uniformly discussed in the context of user interface design. During the history of user interface development, the interaction between humans and computers is almost to be done through software widgets. Since in the last decade, many advanced data visualization and interaction techniques have been developed, now it is the time to bring them into the formal discussion about the context of user interface design, data queries, and data manipulation. The dissertation attempts to fulfill the gap between visual user interface design and interactive data visualization. In relational data queries, many visualization techniques have featured advanced interactive operation; however, a majority of those would concentrate on the traditional style, instead of a modern approach. This is the reason why today in visual analytics truly direct manipulation is highly encouraged, instead of the conventional methods. This dissertation focuses on the investigation of modern data query approaches. It attempts to model the new data query methods that apply those advanced visualization and interaction techniques to facilitate the data analysis procedures. The second contribution of the dissertation is the design of new interaction methods for multi-dimensional data visualization. We first introduce a new framework which includes straightforward manipulation techniques for relational data discovery. These novel techniques, named MCquery, SumUp, and FigAxis, are exclusively developed for the key characteristics of relational data such as data models and data dimensions. The core methodology is about interactive visual query design based upon node-link graphics, parallel coordinate geometries, and scatterplot visualization, where the direct interaction is performed by friendly action such as clicks and brushes. The tools materialized from these techniques can help to reduce users’ cognitive and behavioral effort efficiently in dealing with the issues of information search-retrieval, quantitative data analysis, and correlation examination

    Quantitative Approach on Parallel Coordinates and Scatter Plots for Multidimensional-Data Visual Analytics

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    SumUp : statistical visual query of multivariate data with parallel-coordinate geometry

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    One of the most noticeable issues of parallel coordinate visualization is how to quantitatively analyze density caused by polyline growth in a limited space on axes. The existing visualization tools only support the comparison among single dimensions and single ranges of polylines, which could face limitation in cases of complicated analytics. This paper proposes a new visual-query technique, named SumUp, for statistical analysis of multiple attributes of dimensions and multiple ranges of polylines. The methodology of SumUp is primarily based on developing dynamic queries using brushing operations to deliver summary stacked bars adaptive with parallel coordinates. Users can easily observe quantitative information from data patterns and compare multiple attributes over the density of polylines in the parallel coordinate visualization. Early experiments show that our proposed technique could potentially enhance the manipulation on parallel coordinates, showing by a typical case study

    Interactive data exploration through multiple visual contexts with different data models and dimensions

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    Visual analytics plays a key role in bringing insights to audiences who are interested and dedicated in data exploration. In the area of relational data, many advanced visualization tools and frameworks are proposed in order to dealing with such data features. However, the majority of those have not greatly considered the whole process from data-model mining to query utilizing on dimensions and data values, which might cause interruption to exploration activities. This paper presents a new interactive exploration framework for relational data analysis through automatic interconnection of data models, data dimensions and data values. The basic idea is to construct a relative and switchable chain of those context representations by integrating our previous techniques on node-link, parallel coordinate and scatterplot graphics. This approach enables users to flexibly make relative queries on desired contexts at any stage of exploration for deep data understanding. The result from a typical case study for the framework demonstration indicates that our approach is able to handle the addressed challenge

    Visitor visas for Asian markets:A comparison between Australia and key competitors

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    A Novel Integrating Data Envelopment Analysis and Spherical Fuzzy MCDM Approach for Sustainable Supplier Selection in Steel Industry

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    Supply chain sustainability, which takes environmental, economic, and social factors into account, was recently recognized as a critical component of the supply chain (SC) management evaluation process and known as a multi-criteria decision-making problem (MCDM) that is heavily influenced by the decision-makers. While some criteria can be analyzed numerically, a large number of qualitative criteria require expert review in linguistic terms. This study proposes an integration of Data Envelopment Analysis (DEA), spherical fuzzy analytic hierarchy process (SF-AHP), and spherical fuzzy weighted aggregated sum product assessment (SF-WASPAS) to identify a sustainable supplier for the steel manufacturing industry in Vietnam. In this study, both quantitative and qualitative factors are considered through a comprehensive literature review and expert interviews. The first step employs DEA to validate high-efficiency suppliers based on a variety of quantifiable criteria. The second step evaluates these suppliers further on qualitative criteria, such as economic, environmental, and social factors. The SF-AHP was applied to obtain the criteria’s significance, whereas the SF-WASPAS was adopted to identify sustainable suppliers. The sensitivity analysis and comparative results demonstrate that the decision framework is feasible and robust. The findings of this study can assist steel industry executives in resolving the macrolevel supplier selection problem. Moreover, the proposed method can assist managers in selecting and evaluating suppliers more successfully in other industries

    A Novel Integrating Data Envelopment Analysis and Spherical Fuzzy MCDM Approach for Sustainable Supplier Selection in Steel Industry

    No full text
    Supply chain sustainability, which takes environmental, economic, and social factors into account, was recently recognized as a critical component of the supply chain (SC) management evaluation process and known as a multi-criteria decision-making problem (MCDM) that is heavily influenced by the decision-makers. While some criteria can be analyzed numerically, a large number of qualitative criteria require expert review in linguistic terms. This study proposes an integration of Data Envelopment Analysis (DEA), spherical fuzzy analytic hierarchy process (SF-AHP), and spherical fuzzy weighted aggregated sum product assessment (SF-WASPAS) to identify a sustainable supplier for the steel manufacturing industry in Vietnam. In this study, both quantitative and qualitative factors are considered through a comprehensive literature review and expert interviews. The first step employs DEA to validate high-efficiency suppliers based on a variety of quantifiable criteria. The second step evaluates these suppliers further on qualitative criteria, such as economic, environmental, and social factors. The SF-AHP was applied to obtain the criteria’s significance, whereas the SF-WASPAS was adopted to identify sustainable suppliers. The sensitivity analysis and comparative results demonstrate that the decision framework is feasible and robust. The findings of this study can assist steel industry executives in resolving the macrolevel supplier selection problem. Moreover, the proposed method can assist managers in selecting and evaluating suppliers more successfully in other industries
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