information visualization
科學視覺化是使得一般的三維現象可以觀看與理解，資訊視覺化則是讓統計資料、股市交易、電腦目錄與文件集合等展現樣式、叢集、缺口與分離的部分。能夠提供方向(orientation)與脈絡(context)，選取區域，提供動態回應來發現變化等功能的視覺化呈現，將更具吸引力。根據視覺資訊搜尋真言(Visual Information-Seeking Mantra)「先全面概觀(overview first)，放大並過濾(zoom and filter)，然後選擇細節觀看(then details on demand)」，本研究提出一個資料類型與任務並重的分類架構。
此一分類架構上包含七種資料類型以及七種任務。七種資料類型描述了任務領域的資料物件以及解決問題實的組織情形，包括一維資料、二維資料、三維資料、時間資料、多維資料、樹狀資料與網路資料。
1. 一維資料：一維資料為文件、程式原始碼及按字母排列的名錄等以某種序列方式(sequential manner)組織的線性資料(linear data)。可以提供給使用者的方法包括發現項目的數量、尋找具有某種特徵的項目與觀察某一項目的所有特徵。介面設計的重點則包括整體概觀的呈現、捲動以及選取的方法。
2. 二維資料：二維資料包含地圖、樓層配置或新聞版面等平面資料，二維資料集合內的每一個項目涵蓋整體區域的一部分。可以提供使用者的方法包括發現接鄰的項目、項目相互間的包含情形、項目間的路徑以及計數、過濾和選擇細節觀看等基本任務。
3. 三維資料： 三維資料為分子、人體、建築物等具有體積的真實物件(real-world objects)。可以提供使用者的方法包括發現接鄰、上下、內外等關係以及其他基本任務，並且需要讓使用者在觀看物件時能夠了解它們的位置與方向。
4. 時間資料：在呈現醫療紀錄、專案管理和歷史性資料時會經常使用時間表，與一維資料不同在於時間資料的項目有起始與終止的時間，並且可以彼此重疊。常見的使用方式包括發現一個時間點或一段時期之前開始、之後結束或經過的所有項目。
5. 多維資料：由於資料項目具有n個特徵，大部分的關連式或統計資料庫都被視為多維資料，以n維空間上的點來表現資料項目，以便進行處理。處理的方法有發現樣式(patterns)、叢集(clusters)、變數間的相關(correlations)、差距(gaps)以及分離的部分(outliers)。
6. 樹狀資料：階層式或樹狀結構的特徵為除了根以外，項目集合內的每一個項目都有一個連結連到一個親代項目(parent item)。提供的使用方式，除了針對項目和連結的基本任務以外，還有階層數量、每一項目的子項目數量，結構相似的項目等和結構特性有關的任務。
7. 網路資料： 資料項目連結到不特定的項目時可採用網路資料。除了針對項目和連結的基本任務以外，提供給使用者的方式還有兩個資料項目間的最短路徑或成本最小的路徑或是整個網路上遊歷。
七種任務則包括：概觀集合全體情形(Overview)、放大感興趣的項目(Zoom)、過濾不感興趣的項目(Filter)、選取並取得詳細的資訊(Details-on-demand)、觀看項目間的關連(Relate)、保持過去的動作(History)和抽取部分集合(Extract)。
A useful starting point for designing advanced graphical user interfaces is the Visual Information-Seeking Mantra: overview first, zoom and filter, then details on demand.
This paper offers a task by data type taxonomy with seven data types (one-, two-, three-dimensional data, temporal and multi-dimensional data, and tree and network data) and seven tasks (overview, Zoom, filter, details-on-demand, relate, history, and extracts).
Visual displays become even more attractive to provide orientation or context, to enable selection of regions, and to provide dynamic feedback for identifying changes (for example, a weather map).
Scientific visualization has the power to make ,atomic, cosmic, and common three-dimensional phenomena (such as heat conduction in engines, airflow aver wings, or ozone holes) visible and
comprehensible.
Abstract information visualization has the power to reveal patterns, clusters, gaps, or outliers in
statistical data, stock-market trades, computer directories, or document collections.
To sort out the prototypes and guide researchers to new opportunities, I propose a type by task taxonomy (TTT) of information viisualizations.
I assume that users are viewing collections of items, where items have multiple attributes. In all seven data types (1-, 2-, 3-dimensional data, temporal and multi-dimensional data, and tree and network data) the items have attributes and a basic search task is to select all items that satisfy values of a set of attributes.
The data types are on the left side of the TTT characterize the task-domain information objects and are organized by the problems users are trying to solve.
The tasks across the top of the TTT are task-domain information actions that users wish to perform.
The seven tasks are:
Overview: Gain an overview of the entire collection.
Zoom : Zoom in on items of interest
Filter: filter out uninteresting items.
Details-on-demand: Select an item or group and get details when needed.
Relate: View relationships among items.
History: Keep a history of actions to support undo, replay, and progressive refinement.
Extract: Allow extraction of sub-collections and of the query parameters.
1-dimensional: linear data types include textual documents, program source code, and alphabetical lists of names which are all organized in a sequential manner. Each item in the collection is a line of text containing a string of characters. Additional line attributes might be the date of last update or author name. Interface design issues include what fonts, color, size to use and what overview, scrolling, or selection methods can be used. User problems might be to find the number of items, see items having certain attributes (show only lines of a document that are section titles, lines of a program that were changed from the previous version, or people in a list who are older than 21 years), or see an item with all its attributes.
2-dimensional: planar or map data include geographic maps, floorplans, or newspaper layouts. Each
item in the collection covers some part of the total area and may be rectangular or not. Each item has task-domain attributes such as name, owner, value, etc. and interface-domain features such as size, color, opacity, etc. While many systems adopt a multiple layer approach to dealing with map data, each layer is 2-dimensional. User problems are to find adjacent items, containment of one item by another, paths between items, and the basic tasks of counting, filtering, and details-on-demand.
3-dimensional: real-world objects such as molecules, the human body, and buildings have items with volume and some potentially complex relationship with other items. Computer-assisted design systems for architects, solid modelers, and mechanical engineers are built to handle complex 3-dimensional relationships. Users' tasks deal with adjacency plus above/below and inside/outside relationships, as well as the basic tasks. In 3-dimensional applications users must cope with understanding their position and orientation when viewing the objects, plus the serious problems of occlusion. Solutions to some of these problems are proposed in many prototypes with techniques such as overviews, landmarks, perspective, stereo display, transparency, and color coding.
Temporal: time lines are widely used and vital enough for medical records, project management, or historical presentations to create a data type that is separate from 1-dimensional data. The distinction in temporal data is that items have a start and finish time and that items may overlap. Frequent tasks include finding all events before, after, or during some time period or moment, plus the basic tasks.
Multi-dimensional: most relational and statistical databases are conveniently manipulated as multidimensional data in which items with n attributes become points in a n-dimensional space. The interface representation can be 2-dimensional scattergrams with each additional dimension controlled by a slider (Ahlberg and Shneiderman, 1994). Buttons can used for attribute values when the cardinality is small, say less than ten. Tasks include finding patterns, clusters, correlations among pairs of variables, gaps, and outliers. Multi-dimensional data can be represented by a 3-dimensional scattergram but disorientation (especially if the users point of view is inside the cluster of points) and occlusion (especially if close points are represented as being larger) can be problems. The technique of parallel coordinates is a clever innovation which makes some tasks easier, but takes practice for users to comprehend (Inselberg, 1985).
Tree: hierarchies or tree structures are collections of items with each item having a link to one parent item (except the root). Items and the links between parent and child can have multiple attributes. The basic tasks can be applied to items and links, and tasks related to structural properties become interesting, for example, how many levels in the tree? or how many children does an item have? While it is possible to have similar items at leaves and internal nodes, it is also common to find different items at each level in a tree.
Network: sometimes relationships among items cannot be conveniently captured with a tree structure and it is useful to have items linked to an arbitrary number of other items. While many special cases of networks exist (acyclic, lattices, rooted vs. un-rooted, directed vs. undirected) it seems convenient to consider them all as one data type. In addition to the basic tasks applied to items and links, network users often want to know about shortest or least costly paths connecting two items or traversing the entire network.
Overview: Gain an overview of the entire collection. Overview strategies include zoomed out views of each data type to see the entire collection plus an adjoining detail view. ... Another popular approach is the fisheye strategy (Furnas, 1986) which has been applied most commonly for network browsing (Sarlcar and Brown, 1994; Bartram et al., 1995). The fisheye distortion magnifies one or more areas of the display, but zoom factors in prototypes are limited to about 5. ... Adequate overview strategies are a useful criteria to look for. Along with an overview plus detail (also called context plus focus) view there is a need for navigation tools to pan or scroll through the
collection.
Zoom: Zoom in on items of interest. Users typically have an interest in some portion of a collection, and they need tools to enable them to control the zoom focus and the zoom factor. ... Zooming could be on one dimension at a time by moving the zoom-bar controls or by adjusting the size of the field-of -view box.
Filter: filter out uninteresting items. Dynamic queries applied to the items in the collection is one of the key ideas in information visualization (Ahlberg et al., 1992; Williamson and Shneiderman, 1992). By allowing users to control the contents of the display, users can quickly focus on their interests by eliminating unwanted items.
Details-on-demand: Select an item or group and get details when needed. Once a collection has been trimmed to a few dozen items it should be easy to browse the details about the group or individual items.
Relate: View relationships among items.
History : Keep a history of actions to support undo, replay, and progressive refinement. It is rare that a single user action produces the desired outcome. Information exploration is inherently a process with many steps, so keeping the history of actions and allowing users to retrace their steps is important.
Extract: Allow extraction of sub-collections and of the query parameters. Once users have obtained the item or set of items they desire, it would be useful to be able to extract that set and save it to a file in a format that would facilitate other uses such as sending by email, printing,
graphing, or insertion into a statistical or presentation package. An alternative to saving the set, they might want to save, send, or print the settings for the control widgets.
These ideas are attractive because they present information rapidly and allow for rapid user-controlled exploration. If they are to be fully effective, some of these approaches require novel data structures, high-resolution color displays, fast data retrieval, specialized data structures, parallel computation, and some user training.
I assume that users are viewing collections of items, where items have multiple attributes. In all seven data types (1-, 2-, 3-dimensional data, temporal and multi-dimensional data, and tree and network data) the items have attributes and a basic search task is to select all items that satisfy values of a set of attributes.
The data types are on the left side of the TTT characterize the task-domain information objects and are organized by the problems users are trying to solve.
The tasks across the top of the TTT are task-domain information actions that users wish to perform.
The seven tasks are:
Overview: Gain an overview of the entire collection.
Zoom : Zoom in on items of interest
Filter: filter out uninteresting items.
Details-on-demand: Select an item or group and get details when needed.
Relate: View relationships among items.
History: Keep a history of actions to support undo, replay, and progressive refinement.
Extract: Allow extraction of sub-collections and of the query parameters.
1-dimensional: linear data types include textual documents, program source code, and alphabetical lists of names which are all organized in a sequential manner. Each item in the collection is a line of text containing a string of characters. Additional line attributes might be the date of last update or author name. Interface design issues include what fonts, color, size to use and what overview, scrolling, or selection methods can be used. User problems might be to find the number of items, see items having certain attributes (show only lines of a document that are section titles, lines of a program that were changed from the previous version, or people in a list who are older than 21 years), or see an item with all its attributes.
2-dimensional: planar or map data include geographic maps, floorplans, or newspaper layouts. Each
item in the collection covers some part of the total area and may be rectangular or not. Each item has task-domain attributes such as name, owner, value, etc. and interface-domain features such as size, color, opacity, etc. While many systems adopt a multiple layer approach to dealing with map data, each layer is 2-dimensional. User problems are to find adjacent items, containment of one item by another, paths between items, and the basic tasks of counting, filtering, and details-on-demand.
3-dimensional: real-world objects such as molecules, the human body, and buildings have items with volume and some potentially complex relationship with other items. Computer-assisted design systems for architects, solid modelers, and mechanical engineers are built to handle complex 3-dimensional relationships. Users' tasks deal with adjacency plus above/below and inside/outside relationships, as well as the basic tasks. In 3-dimensional applications users must cope with understanding their position and orientation when viewing the objects, plus the serious problems of occlusion. Solutions to some of these problems are proposed in many prototypes with techniques such as overviews, landmarks, perspective, stereo display, transparency, and color coding.
Temporal: time lines are widely used and vital enough for medical records, project management, or historical presentations to create a data type that is separate from 1-dimensional data. The distinction in temporal data is that items have a start and finish time and that items may overlap. Frequent tasks include finding all events before, after, or during some time period or moment, plus the basic tasks.
Multi-dimensional: most relational and statistical databases are conveniently manipulated as multidimensional data in which items with n attributes become points in a n-dimensional space. The interface representation can be 2-dimensional scattergrams with each additional dimension controlled by a slider (Ahlberg and Shneiderman, 1994). Buttons can used for attribute values when the cardinality is small, say less than ten. Tasks include finding patterns, clusters, correlations among pairs of variables, gaps, and outliers. Multi-dimensional data can be represented by a 3-dimensional scattergram but disorientation (especially if the users point of view is inside the cluster of points) and occlusion (especially if close points are represented as being larger) can be problems. The technique of parallel coordinates is a clever innovation which makes some tasks easier, but takes practice for users to comprehend (Inselberg, 1985).
Tree: hierarchies or tree structures are collections of items with each item having a link to one parent item (except the root). Items and the links between parent and child can have multiple attributes. The basic tasks can be applied to items and links, and tasks related to structural properties become interesting, for example, how many levels in the tree? or how many children does an item have? While it is possible to have similar items at leaves and internal nodes, it is also common to find different items at each level in a tree.
Network: sometimes relationships among items cannot be conveniently captured with a tree structure and it is useful to have items linked to an arbitrary number of other items. While many special cases of networks exist (acyclic, lattices, rooted vs. un-rooted, directed vs. undirected) it seems convenient to consider them all as one data type. In addition to the basic tasks applied to items and links, network users often want to know about shortest or least costly paths connecting two items or traversing the entire network.
Overview: Gain an overview of the entire collection. Overview strategies include zoomed out views of each data type to see the entire collection plus an adjoining detail view. ... Another popular approach is the fisheye strategy (Furnas, 1986) which has been applied most commonly for network browsing (Sarlcar and Brown, 1994; Bartram et al., 1995). The fisheye distortion magnifies one or more areas of the display, but zoom factors in prototypes are limited to about 5. ... Adequate overview strategies are a useful criteria to look for. Along with an overview plus detail (also called context plus focus) view there is a need for navigation tools to pan or scroll through the
collection.
Zoom: Zoom in on items of interest. Users typically have an interest in some portion of a collection, and they need tools to enable them to control the zoom focus and the zoom factor. ... Zooming could be on one dimension at a time by moving the zoom-bar controls or by adjusting the size of the field-of -view box.
Filter: filter out uninteresting items. Dynamic queries applied to the items in the collection is one of the key ideas in information visualization (Ahlberg et al., 1992; Williamson and Shneiderman, 1992). By allowing users to control the contents of the display, users can quickly focus on their interests by eliminating unwanted items.
Details-on-demand: Select an item or group and get details when needed. Once a collection has been trimmed to a few dozen items it should be easy to browse the details about the group or individual items.
Relate: View relationships among items.
History : Keep a history of actions to support undo, replay, and progressive refinement. It is rare that a single user action produces the desired outcome. Information exploration is inherently a process with many steps, so keeping the history of actions and allowing users to retrace their steps is important.
Extract: Allow extraction of sub-collections and of the query parameters. Once users have obtained the item or set of items they desire, it would be useful to be able to extract that set and save it to a file in a format that would facilitate other uses such as sending by email, printing,
graphing, or insertion into a statistical or presentation package. An alternative to saving the set, they might want to save, send, or print the settings for the control widgets.
These ideas are attractive because they present information rapidly and allow for rapid user-controlled exploration. If they are to be fully effective, some of these approaches require novel data structures, high-resolution color displays, fast data retrieval, specialized data structures, parallel computation, and some user training.
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