information visualization
先前進行資訊視覺化分類技術分類架構的研究[Shneiderman96, Chi97, North98, CMS99]時，研究者大多從相容於技術的資料領域(data domains)著手，以資料為中心的觀點進行分析。例如：[CMS99]擴充Card and Mackinlay 的資料導向分類架構[Card97]，將視覺化領域區分為科學視覺化(Scientific Visualization)、地理資訊系統(GIS)、多維圖(Multi-dimensional Plots)、多為表格(Multi-dimensional Tables)、資訊地景與空間(Information Landscapes and Spaces)、節點與連結(Node and Link)、樹狀圖(Trees)和文本轉換(Text Transforms)等子類別。由於在應用上實作的人員可以快速地確認分析所需的各種技術，這樣的方式大多被認為是有用的。然而這些方法並無法讓實作人員了解如何應用與實做這些技術。
本研究使用資料狀態模型(data state model)，提出資訊視覺化分類技術分類架構的新方法，此一新方法從資訊視覺化技術的資料類型(data type)與處理的運作步驟(processing operating steps)，對資訊視覺化的設計空間(design space)進行目前最詳細與最全面的分析，不僅讓研究人員可以了解設計的空間，也可以幫助實作人員了解如何資訊視覺化技術應用得更廣泛。此一分類架構包含四個資料層次、三種在不同層次間的資料結構進行轉換的資料轉換(data transforms)以及四種不改變資料結構在層次內的運作(with stage operations)。四個資料層次與三種資料轉換的說明分別如表 1和表2。
表 1 資料層次
層次 (Stage)
|
描述
|
數值 (Value)
|
原始資料 (The raw data)
|
分析性抽象化 (Analytical Abstraction)
|
關於資料的資料或者資訊，也就是後設資料(meta-data.)
|
視覺抽象化 (Visualization Abstraction)
|
可以使用視覺化技術使其可視於螢幕的資訊
|
視圖 (View)
|
最後呈現給使用者觀看與理解的視覺化映射結果(visualization mapping)
|
表 2 資料轉換
| 處理步驟 (Processing Step) | 描述 |
| 資料轉換 (Data Transformation) | 從數值中產生分析性抽象化的形式(通常透過抽取)。 |
| 視覺化轉換 (Visualization Transformation) | 取得分析性抽象化並將其。Takes an analytical abstraction and further reduces it into some form of visualization abstraction, which is visualizable content. |
| 視覺映射轉換 (Visual Mapping Transformation) | Takes information that is in a visualizable format and presents a graphical view. |
這種分類架構將資料模組間的相依性(dependencies)獨立出來，清楚地指出資料和運算間的互動，有助於了解各種視覺化技術的相似性與差異，並且建構新的視覺化與互動方式，具有可以再利用(reuse)既有運算的優點。
In previous work, researchers have attempted to construct taxonomies of information visualization techniques by examining the data domains that are compatible with these techniques.
This is useful because implementers can quickly identify various techniques that can be applied to their domain of interest.
However, these taxonomies do not help the implementers understand how to apply and implement these techniques.
In this paper, we will extend and then propose a new way to taxonomize information visualization techniques by using the Data State Model [Chi98].
The paper shows that the Data State Model not only helps researchers understand the space of design, but also helps implementers understand how information visualization techniques can be applied more broadly.
There have been several efforts to produce various information visualization taxonomies [Shneiderman96, Chi97, North98, CMS99].
In this paper, we will present a detailed analysis of a large number of visualization techniques using the Data State Model. The contribution is that our analysis of the information visualization design space is the most detailed and thorough to date. It is more detailed in the sense that we have broken each technique down by not only its data type, but also by its processing operating steps.
Most previous work focused on constructing taxonomies of information visualization techniques
uses a data-centric point of view.
In an article describing the design space of information visualization techniques, Card and Mackinlay started constructing a data-oriented taxonomy [Card97], which is subsequently expanded in [CMS99]. This taxonomy divides the field of visualization into several subcategories: Scientific Visualization, GIS, Multi-dimensional Plots, Multi-dimensional Tables, Information Landscapes and Spaces, Node and Link, Trees, and Text Transforms.
OLIVE is a taxonomy assembled by students in Shneiderman’s information visualization class [Olive99], and divides information visualization techniques using eight visual data types: temporal, 1D, 2D, 3D, multi-D, Tree, Network, and Workspace.
Figure 1 shows an overview of the Data State Model [Chi98], which breaks down each technique into four Data Stages, three types of Data Transformation and four types of Within Stage operators.
The visualization data pipeline is broken into four distinct Data Stages: Value, Analytical Abstraction, Visualization Abstraction, and View (See Table 1).
| Stage | Description |
| Value | The raw data. |
| Analytical Abstraction | Data about data, or information, a.k.a. meta-data. |
| Visualization Abstraction | Information that is visualizable on the screen using a visualization technique. |
| View | The end-product of the visualization mapping, where the user sees and interprets the picture presented to her. |
Transforming data from one stage to another requires one of the three types of Data Transformation operators: Data Transformation, Visualization Transformation, and Visual Mapping Transformation (Table 2).
| Processing Step | Description |
| Data Transformation | Generates some form of analytical abstraction from the value (usually by extraction). |
| Visualization Transformation | Takes an analytical abstraction and further reduces it into some form of visualization abstraction, which is visualizable content. |
| Visual Mapping Transformation | Takes information that is in a visualizable format and presents a graphical view. |
Within each Data Stage, there are also operators that do not change the underlying data structures. These are the Within Stage Operators, of which there are four types, corresponding to the four Data Stages: Within Value, Within Analytical Abstraction, Within Visualization Abstraction, and Within View.
(1) some operators create new kinds of data sets, whereas some operators create filtered subsets, which is the difference between Transformation and Within Stage operators,
(2) that the same Visualization Abstractions can be mapped using a variety of Visual Mapping Transformation operators.
By isolating dependencies, we can more easily reuse different parts of the pipeline to construct new information visualizations.
With a clearer understanding of the interactions between the data and the operators, implementers will be more equipped to construct new interactions or new visualizations.
For each of the visualization techniques, the results of the analysis help us classify and choose how to implement the different operators in a large visualization system. For example, many hierarchical techniques share similar operating steps that can be standardized in a system. Implementers may take advantage of these similarities.
For implementers, the taxonomy also directly specifies the sequential ordering of operators that are possible in a given visualization technique. In this way, it specifies the system module dependencies that are induced between the operators. Knowing these dependencies enables implementers to better organize their system for modularity.
This is because the Data State Model helps categorization and taxonomization, which expose the dependencies between visualization modules and the similarities and differences among visualization techniques.
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