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YANG Yanbo,LIU Bin,QI Mingyue.Review of information visualization[J].Journal of Hebei University of Science and Technology,2014,35(1):91-102
信息可視化研究綜述
Review of information visualization
Received:October 16, 2013  Revised:November 20, 2013
DOI:10.7535/hbkd.2014yx01016
中文關鍵詞:  信息可視化  可視化技術  人機交互  數據挖掘
英文關鍵詞:information visualization  visualization technology  human-machine interaction  data mining
基金項目:國家自然科學基金(71271076)
Author NameAffiliation
YANG Yanbo School of Economics and Management, Hebei University of Science and Technology 
LIU Bin School of Economics and Management, Hebei University of Science and Technology 
QI Mingyue Communication Station of Hebei Provincial Military Command 
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中文摘要:
      信息可視化是可視化技術在非空間數據領域的應用,可以增強數據呈現效果,讓用戶以直觀交互的方式實現對數據的觀察和瀏覽,從而發現數據中隱藏的特征、關系和模式?梢暬瘧梅浅V泛,主要涉及領域:數據挖掘可視化、網絡數據可視化、社交可視化、交通可視化、文本可視化、生物醫藥可視化等等。根據CARD可視化模型可以將信息可視化的過程分為以下幾個階段:數據預處理;繪制;顯示和交互。根據SHNEIDERMAN的分類,信息可視化的數據分為以下幾類:一維數據、二維數據、三維數據、多維數據、時態數據、層次數據和網絡數據。其中針對后4種數據的可視化是當前研究的熱點。多維數據可視化方法主要包括基于幾何的方法、圖標方法和動畫方法等;趲缀蔚目梢暬绞街凶罱浀涞木褪恰捌叫凶鴺讼怠狈椒。平行坐標系(parallel coordinates)使用平行的豎直軸線來代表維度,通過在軸上刻劃多維數據的數值并用折線相連某一數據項在所有軸上的坐標點展示多維數據。平行坐標系方法能夠簡潔、快速地展示多維數據,發展出很多改進技術。但是當數據集的規模變得非常大時,密集的折線會引起“視覺混淆”(visual clutter),處理方法包括維度重排、交互方法、聚類、過濾、動畫等。其他基于幾何的方法包括Radviz方法使用圓形坐標系展示可視化結果;散點圖矩陣(scatter plot matrix)將多維數據中的各個維度兩兩組合繪制成一系列的按規律排列的散點圖;趫D標的可視化方法用具備可視特征的幾何形狀如大小、長度、形狀、顏色等刻劃數據,代表性的方法包括星繪法和Chernoff 面法等。動畫方法用于可視化中可被用來提高交互性和理解程度,其缺點包括可能分散注意力、引起用戶的誤解、產生“圖表垃圾”等。時間序列數據是指具有時間屬性的數據集,針對時間序列數據的可視化方法如下:線形圖、堆積圖、動畫、地平線圖、時間線。層次數據具有等級或層級關系。層次數據的可視化方法主要包括節點鏈接圖和樹圖2種方式。其中樹圖(treemap)由一系列的嵌套環、塊來展示層次數據。為了能展示更多的節點內容,一些基于“焦點+上下文”技術的交互方法被開發出來。包括“魚眼”技術、幾何變形、語義縮放、遠離焦點的節點聚類技術等。網絡數據具有網狀結構。自動布局算法是網絡數據可視化的核心,目前主要有以下3類:一是力導向布局(force-directed layout);二是分層布局(hierarchical layout);三是網格布局(grid layout)。當數據節點的連接很多時,容易產生邊交叉現象,導致視覺混淆。解決邊交叉現象的集束邊(edge bundle)技術可以分為以下幾類:力導向的集束邊技術、層次集束邊技術、基于幾何的邊聚類技術、多層凝聚集束邊技術和基于網格的方法等。其他研究熱點包括圖形的視覺因素研究、自適應可視化研究、可視化效果的評估等。視覺因素對于可視化效果的影響,如位置、長度、面積、形狀、色彩等影響已經引起很多研究者的注意。色彩是視覺因素的重要組成部分,研究主要集中在顏色選擇的原則和交互系統中。這些原則基于數據類型、類的數量、認知約束等。自適應可視化可以提高信息可視化的適應性。研究成果分為以下幾類:自適應可視化展示、自適應資源模型、自適應用戶模型。自適應可視化展示是指根據用戶的特征自動為用戶提供多種展示類型,自動選擇可視化內容及布局的形式,自動調整可視化的元素等。自適應資源模型反映了對硬件和軟件的利用以提高可視化性能。自適應用戶模型通過顯示用戶模型的內容并讓用戶能夠編輯,從而讓用戶能夠控制模型的內容。當前關于信息可視化評價的研究較少,少量研究也沒有提出直接和通用的可視化的評估方式,需要對信息可視化評價的理論基礎、方法和應用做深入的研究?梢暬夹g與應用還應該繼續向以下4個方面努力:直觀化、關聯化、藝術化、交互化。信息可視化技術的發展方向是協同(collaboration)、分析過程(analytics)、計算(computational)和意會(sense-making)。未來研究方向可以包括以下幾個內容。信息可視化和數據挖掘的緊密結合。為提高處理海量數據時的速度和效率和解決視覺混淆現象;必須運用數據挖掘的公式和算法,對數據分析的過程及結果進行可視化展現。協同可視化。協同可視化領域的研究方向可以包括可視化接口設計、基于Web的可視化協同平臺開發、協同可視化工作的視圖設計、協同可視化中的工作流管理及協同可視化技術的應用等。更多領域的應用技術開發。包括統計可視化:需要研究使用幾何、動畫、圖像等工具對數據統計的過程和結果進行加工和處理的技術;新聞可視化:對新聞內容進行抓取、清洗和提取和可視化展示;社交網絡可視化:可視化方式顯示社交網絡的數據,對社交網絡中節點、關系及時空數據的集成展示。搜索日志可視化:針對在使用搜索引擎時產生的海量搜索日志,可視化的展現用戶的搜索行為、關系和模式等。
英文摘要:
      Information visualization is the application of visualization technology in non-spatial data area, enhancing data presentation effect. Users can observe the data intuitively and interactively so as to find implicit features, relations and patterns in data. The application of information visualization is very abroad which includes data mining visualization, network data visualization, social data visualization, traffic visualization, text visualization, and medicine visualization, etc. According to Card model on information visualization, process of information visualization includes three stages: data pretreating, data plotting, data displaying and interacting. Ben Shneiderman notes that visualization data includes one-dimensional data, two-dimensional data, three-dimensional data, multi-dimensional data, temporal data, hierarchical data, and network data, of which are given much attention to research. Visualization methods of multi-dimensional data include geometry methods, icon methods, and animation methods, etc. Among the geometry-based visualization methods, the most classic one is the parallel coordinates approach. It uses parallel vertical axis to represent the dimension values. By the multidimensional data portrayed on the shaft, and by the coordinate point connected with a line to a data entry on the axes, the multidimensional data was presented. Multi-dimensional data was displayed concisely and quickly in Parallel Coordinates, and improved many techniques. When scale of data set was very large, the dense lines could cause visual clutter. The methods of clutter reduction include dimension reordering, interacting, clustering and filtering, and visual enhancement, etc. Other methods based on geometry, including Radviz(Radial Coordinate visualization), display multi-dimensional data by circular coordinate. Scatter plot matrix arranges every demensions of multidimensional data to be combined into pairwise mode, drawing a series of regular scatters. Icon was used to describe the multi-dimensional data by its geometrical features including size, length, form and color, etc. Icon methods include star graph and Chernoff face method. Animation used for visualization can improve the degree of interacting and understanding. , but with shortcomings such as: distraction, misunderstanding and visual clutter. Time serial data refers to data sets with time property. The visualization methods include line chart, stock chart, animation, horizon graph and Timeline. Hierarchical data can be used to describe object whose attributes are rank and level. Its visualization methods include linking point graph and tree map. Tree map displays hierarchical data by nesting hoop and lump. For displaying more content, based on "Focus+Content" technology, some methods were put forward including "fish eyes" technology, geometry deformation, Semantic zooming and clustering. Network data has network structure. Layout algorithm is the core of visualization of network data, which includes three classes: Force-Directed Layout, Hierarchical Layout and Grid Layout. When there're many data connection nodes, edge corssover phenomenon happens, causing visual confusion. There were a variety of techniques for resolving the edge bundling, including hierarchical edge bundling, force-directed edge bundling, geometry-based edge clustering, multi-level agglomerative edge bundling, and grid-based methods. Other research hotspots include research on visual feature, adaptive visualization and evaluation of information visualization. Effect of visual feature such as position, length, area, shape and color, etc. on visual result has received considerable attention. Color is one of most important visual factor, so research focuses on the color selection principle and interaction system, which are based on data type, quantity, and cognitive constraints. Adaptive visualization can enhance adaptability of information visualization, which includes adaptive display, adaptive resource model, and adaptive user model according to research of Domik & Gutkauf and Grawemeyer & Cox. Adaptive display provides automatic and suitable display for different users, including selecting content and layout, adjusting visual features automatically. Adaptive resource model means utilizing hardware and software to enhance visual performance. Adaptive user model means displaying user model in order to edit and control content. Morse et al. notes that the research on evaluation of information visualizations is rare. Evaluation on direct and general information visualization was not involved in some research. So, it is needed to do deep research on the theoretical basis, method and application of information visualization evaluation. Technology and application of information visualization should be developed in four aspects, displaying data directly perceived through the senses, mining and showing relation between data, strengthening demonstration of aesthetics and artistry, enhancing performance of interaction and operation on real-time data. Dai et al. noted that research direction of information visualization was Collaboration, Analytics, Computational and Sense-making. Research directions in future is as following. Visualization and data mining: to promote efficiency and avoid visual clutter in processing huge data, information visualization should be combined with data mining so that user can operate huge data and discover implicit information. Collaborative visualization: Collaborative visualization includes interface design, collaborative platform based on web, view design, workflow design, and application of technology. Application in more fields: statistics visualization refers to processing and handling the statistical process data and results by method of geometry, animation, and graph ett. News visualization refers to presenting diversely analysis results after grasping, cleaning, and drawing news corpus. Social network visualization refers to displaying and revealing relation, comparison, and trend of social network through integration of dimensions of time and space. Search log visualization refers to displaying huge searching behavior when using a search engine. Users' search behavior, relationships and patterns are presented visually.
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