Ideas behind the INSYDER visualization components

The motive for the use of visualizations beyond pure presentation in list form was to improve ac-cess to the abstract result sets from WWW-searches following the classic goal of “Information Visualization”. The final implementation of the INSYDER system included five components for the presentation of search results: a HTML-List, a ResultTable, a ScatterPlot, a BarGraph, and a SegmentView with two modes: TileBars and StackedColumn. For details-on-demand functions there are also a segment tooltip, a document tooltip, a text window, and a browser. Why were these visualizations chosen for implementation? The main considerations were:

• Focus on the visualization of the search results,

• Multiple Coordinated View approach,

• Orientation on Business Graphics.

In general, the development of the INSYDER system followed a user-centered approach. The vis-ual representations are focused on the review-of-results phase of the four-phase framework, since this is the most interesting one from the user’s point of view. Here the user gets the suggestions satisfying his information need, and it would be a good idea to help him find the needle in the hay-stack by applying suitable visualizations. On the result set level, an overview of all search results to identify which documents fit best with the user’s information needs would be useful. On the document level, the user is interested in seeing which parts of a document fit best with his information need. The general design principle was to support the review of the results phase following the visual-information-seeking mantra: Overview first, zoom and filter, then details on demand [Shneiderman 1998]. Besides the visualizations for the review of results phase, that are documented in this thesis, other visual views used in INSYDER support the interaction of the user with the system during the formulation of the query (e.g. visualization of related terms of the query terms by a graph), and during the refinement of the query (e.g. visualization of new query terms based on a relevance feedback inside the graph representing the query terms). These parts of the system are documented in [Mußler, Reiterer, Mann 2000], and [Mußler 2002].

An important design decision for the result phase was to use a multiple view approach. This is in harmony with the rule of Diversity (i.e. use multiple views when there is a diversity of attributes, models, user profiles, levels of abstraction, or genres) from [Baldonado, Woodruff, Kuchinsky 2000]. Knowing that there is no “best visualization” and that the success of a specific visualization depends on several factors including the target user group, the current task, and the type and num-ber of data, we decided to use a combined approach. As shown in Chapter 3.4, the visualization of search results is a natural candidate for multiple view approaches because of the variety of differ-ent levels of abstraction necessary to deal with search results ranging from overviews about the whole result set to detailed views of documents and their parts. Multiple view approaches offer the user the possibility to choose the most appropriate visualization view for his current demand or

individual preferences. Due to the restrictions involved in running the software on standard busi-ness PCs with 17-inch- or sometimes only 15-inch-screens a space-multiplexed approach simulta-neously showing different visualizations was dismissed. Instead, a time-multiplexed solution ar-ranging the different components on tabbed panes was implemented. To avoid the possible draw-backs of multiple view approaches several guidelines have been considered. The number of used visualizations has been reduced to a small number. This accords with the rule of Parsimony (i.e.

use multiple views minimally) from [Baldonado, Woodruff, Kuchinsky 2000]. Only simple visu-alizations have been chosen. Feedback from real users has been used to make the final choice and improvements of the selected visualizations. The visual structures have been adapted to each other in color, orientation, and the overall style. The visualizations are synchronized in such a way that a selection in one representation of the result set will be updated immediately in the other represen-tations too. These points are in harmony with the rules of Self-Evidence (i.e. use perceptual cues to make relationships among multiple views more apparent to the user) and Consistency (i.e. make the interfaces for multiple views consistent and make the states of multiple views consistent) from [Baldonado, Woodruff, Kuchinsky 2000]. In addition, grouping of the chosen visualizations around the traditional result list was planned. This should have been the default view, because it is the most familiar one for many users. The visualizations should have been ordered with an increas-ing level of detail information from the left to the right, with the list positioned in the middle of this row. Figure 111 and some of the prototypes in the last chapter show the initial ideas. These figures contain in some cases visualization ideas not ultimately included in the system. These omitted components are discussed below.

Vector Scatterplot Bargraph List Tilebars Rel. Curve Thumbnails

Figure 111: Navigation concept

In the implemented version of the software, the HTML-List or the ResultTable have been kept as the default view, because they are familiar to many users. The ordering with increasing levels of detail from left to right has also been kept, except for the List and the Table that are positioned now at the beginning of the row. Figure 112 shows the final implementation.

Figure 112: Tabbed pane

Not naming it explicitly Attention Management, in keeping with the rule from [Baldonado, Wood-ruff, Kuchinsky 2000] of using perceptual techniques to focus the user’s attention on the right view at the right time, there was a discussion in the project about whether techniques to automati-cally select the mapping from data tables to visual structures should be used. Automatic selection, as for example by [Andrienko, Andrienko 1997] for the visualization of data with geographical elements, goes even one step further than attention management. This approach was not used for the INSYDER project, because we felt that we are still far from having enough insight into the efficiency and the effectiveness of certain visualizations in certain situations when dealing with the visualization of search results. Instead, the approach chosen was to offer the user different visuali-zations, that he can select, and if necessary also combine sequentially, according to his current situation.

The visual-information-seeking system INSYDER is not a general purpose system like traditional search engines (e.g. AltaVista). Its context of use is to support small- and medium-sized enter-prises of specific application domains finding business information in the Web. Accordingly, the findings of general empirical studies like those mentioned above are in principle useful but had to be supplemented with more specific requirements. At the beginning of the project, a field study was conducted using a questionnaire that has been answered by 73 selected companies (SMEs) in Italy, France, and Great Britain. The aim was to understand the context of use [ISO 9241-11] in keeping with a human-centered design approach [ISO 13407]. The following requirements are based on this field study. The typical users of the INSYDER system are experts from business domains like CAD software or building and construction. These two business domains had been chosen as test areas in the project. Experts from these domains are typically not specialists in using information retrieval systems. They are familiar with the Web and have some limited understand-ing of search engines. The scenarios show the typical information sources, the typical information needs of the users (e.g. data about new technologies, data about the market, technical regulations, and call for tenders), and the expected functionality (search, monitoring, portal for news). These results correspond very well to an empirical study conducted by [Choo, Detlor, Turnbull 1999], which show that information seekers on the Web typically use a combination of start pages (news or portal sites), a regular check of selected pages (monitoring), and a systematic work through several search engines or meta search engines. Our field study showed that the information needs are normally formulated in unstructured text. The typical technical environments of the users are business PCs. The study showed that the processing power, the RAM, and the size of the screen are limited. It was therefore not possible to use sophisticated 3D visual structures only available on high-end PCs or special workstations. Based on the experiences of the field study, different task scenarios using an information-seeking system like INSYDER to find business information have been developed. The final selection of the visual structures was based on the above suggestions of the field study, an extensive study of the state-of-the-art in visualizing text documents, that is par-tially documented in Chapter 3.3, and the design goal of orienting our visual structures as much as possible on typical business graphics. The field study showed that all users have a good under-standing of this kind of graphics and use them during their daily work (e.g. in spreadsheet pro-grams). Similar conclusions, based mainly on an overview of the research done in the area of visu-alization of search results in document retrieval systems, can be found in [Zamir 1998]. The author suggested that for a document visualization technique to appear on the Web. Additionally the visu-alization must be very easy for novice users to understand; it must require minimal CPU time and other resources; and it must be useful for a considerable proportion of searches performed on the Web. Systems that relate the documents to the query terms (like bar charts, tile bars) or to prede-fined document attributes (like scatter plots) seem to be useful visualization techniques providing additional information about retrieved Web documents.

It was not the intention during the development of the INSYDER system to come up with new visual metaphors supporting the retrieval process. The main idea was to select existing visualiza-tions for text documents and to combine them in a novel way. We tried to select expressive visu-alizations keeping in mind the target users (business analysts), their typical tasks (to find business data in the Web), their technical environment (typically a desktop PC and not a high-end work-station for extraordinary graphic representations), the type of data to be visualized (document sets and text documents), and minimal necessary training. The major challenge from our point of view was to combine intelligently the selected visualization supporting different views on the retrieved

document set and the documents themselves. The primary intention was to present additional formation about the retrieved documents to the user in a way that is intuitive, may be quickly in-terpreted, and can scale to large document sets.