RESEARCH SAMPLES PAPERS PEOPLE HOME |
|||

2. SAGE : A System for Automatic Graphical Explanations SAGE is a knowledge-based presentation system that designs graphical displays of combinations of diverse information (e.g., quantitative, relational, temporal, hierarchical, categorical, geographic). The inputs to SAGE include: (1) sets of data represented as tuples in a relational database, (2) a characterization of the properties of the data that are relevant to graphic design, and (3) an optional set of design specifications, expressing a user's preferences for visualizing the data set. SAGE’s output consists of one or more coordinated sets of 2-D information graphics that use a variety of graphical techniques to integrate multiple data attributes in a single display. SAGE integrates multiple attributes in three ways: - by representing them as different properties of the same set of graphical objects. For example, both the left and the right edges of the bars in the left most chart in Figure 2 are used to map attributes (
*asking-price*and*selling-price*respectively)
- by assembling multiple graphical objects into groups that function as units to express data. For example, the interval bar and the mark in the left most chart in Figure 2 are used to show different types of price-related attributes:
*asking-price,**selling-price*and the*agency-estimate.*
- by aligning multiple charts and tables together with respect to a common axis. For example, the three charts in Figure 2 are aligned on the Y-axis which indicates the house.
Creating a graphic that integrates data in this way is partly an encoding process in which the values of data attributes are converted into graphical values of properties of objects (e.g., color, shape, and spatial position of polygons). Interpreting the information in a graphic is a decoding process, where people must translate visual symbols back into data values. SAGE creates graphics that enable people to perform efficiently information-seeking tasks (e.g., searching for clusters of data values that are different from the rest and looking up other facts to understand what makes them different). However, in designing graphics, SAGE only considers how effectively attributes can be mapped to graphical properties to support a task. For example, a requirement to be able to search for particular values by name might result in the relevant attribute being arranged along an axis in lexicographic order; on the other hand, if it is important to find the maximum and minimum values in a set, SAGE might order these values in terms of magnitude. SAGE, like other automated presentation systems (Casner 1991, Mackinlay 1986,), does SAGE can also design complex presentations that have overlapping objects, or use cluster composition to define a novel combination or grouping of graphical objects in the presentation. This can make under-standing some of the graphics that SAGE generates quite difficult. Fortunately, the picture representation used in SAGE contains a complete declarative representation of the content and structure of the graphic in a form that can be used for reasoning by other components. Thus, this representation can be used to reason about possible sources of user confusion arising from mappings that are either complex or ambiguous to the user. SAGE 's representation serves three functions in explanation generation. First, it helps define what a viewer must understand about a graphic in order to obtain useful information from it. It does this by defining the elements of a graphic and the way they combine to express facts (i.e., how they map to data). Second, the representation describes the structure of both the graphical presentation and the data it presents, so that they can be explained coherently. Finally, the representation helps derive judgments of Figure 3
Figure 4
Symbol classes in SAGE can be either pre-defined (some of the more common ones, such as a labeled-mark have already been defined), or created by the system based on rules about combining different graphemes into clusters.
In addition to this knowledge about graphemes, symbols and encoders, SAGE also uses knowledge of the characteristics of data relevant to graphic design (Roth and Mattis, 1990, Roth and Hefley 1993), including knowledge of data types and scales of measurement (e.g., quantitative, interval, ordinal, or nominal data sets), structural relationships among data (e.g., the relation between the end-points of ranges or between the two coordinates of a 2-D geographic location), and the functional dependencies among attributes in database relations (e.g., one:one, one:many, many:many). As we will show later, the latter is an important factor in selecting a high-level discourse strategy for generating explanatory captions. Finally, SAGE has a library of graphical techniques, knowledge of the appropriateness of the techniques for different data and tasks, and design knowledge for assembling these techniques into composites that can integrate information in a single display. SAGE uses this graphic design knowledge together with the data characterization knowledge to generate displays of information. To summarize, the portion of SAGE’s knowledge base that is most relevant for generating explanatory captions is its graphical syntax and semantics. The syntax includes a definition of the graphical constituents that convey information: spaces (e.g., charts, maps, tables), (graphemes) (e.g., labels, marks, bars) , their properties (e.g. color, shape), and encoders--the frames of reference that enable their properties to be interpreted/translated back to data values--(e.g., axes, graphical keys.). The syntax also defines the ways in which graphemes can be combined to form symbols--composites that integrate multiple data attributes (e.g., a label attached to a mark). The syntactic structure of a graphical display, like the linguistic structure of text, can provide guidance for creating structurally coherent explanations. The representation of the To next section. |
|||

## Paper Sections:To Title pageTo Part 1: Introduction To Part 3: Discourse Strategies for Generating Captions To Part 4: Graphical Complexity: The Need for Clarification To Part 5: Generating Explanatory Captions To Part 6: System Implementation and Evaluation To Part 7: Related Work To Part 8: Conclusions and Future Work To Appendix A To Acknowledgements |
|||

[RESEARCH] [SAMPLES] [PAPERS] [PEOPLE] [HOME] |