Olga Kulyk

Tijs De Kler

Wim De Leeuw

Gerrit Veer

Betsy Van Dijk

Gerrit Van Der Veer

Staying Focused: Highlighting-on-Demand as Situational Awareness Support for Groups in Multidisplay Environments

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The complexity of communication processes in a co-located decision-making environment requires the combination of several approaches to support situational awareness. This, in turn, requires a practical method to capture and analyse the dynamics of technology-mediated interactions in context. The nature of the interfaces as well as the physical characteristics and affordances of the environment influence the way in which interactions occur [START_REF] Fruchter | Developing methods to understand discourse and workspace in distributed computer-mediated interaction[END_REF].

Therefore our approach for data analysis includes a combination of behaviour, interaction and environment analysis.

We will assess shared situational awareness of team members when we provide supportive visualisations on a shared large display. We aim at reducing disturbing factors that are considered a distraction from the primary group decision-making task. We intend to establish an indication of the relations between situational awareness, team satisfaction, group processes like decision making and the perceived task performance. Video recordings from several viewpoints will enable us to analyse several simultaneously ongoing interactions. In addition to observations, post-interviews and questionnaires are carried out to obtain subjective judgments of the team members, for example, on group satisfaction, awareness and distraction from primary tasks [START_REF] Cadiz | Designing and deploying an information awareness interface[END_REF][START_REF] Kulyk | Do you know what i know? situational awareness and scientific teamwork in collaborative environments[END_REF][START_REF] Olaniran | A model of group satisfaction in computer-mediated communication and face-to-face meetings[END_REF]. Group satisfaction will be assessed by a combined validated post-task questionnaire featuring the group process and decision making [START_REF] Olaniran | A model of group satisfaction in computer-mediated communication and face-to-face meetings[END_REF]. We apply these questions to assess the perceived usefulness and impact of the Highlighting-on-Demand interface on the shared situational awareness of team members, on distraction from the primary task, and on team satisfaction with the group process and decision-making process.

Theory Grounding

Situational Awareness

Situational awareness is expected to be an important determinant of team performance [START_REF] Bolstad | Modeling shared situation awareness[END_REF]Endsley, 1995a]. SA provides the "primary basis for subsequent decision making and performance in the operation of complex, dynamic systems..." [Endsley, 1995a]. At its lowest level the team member needs to perceive relevant information (in the environment, system, self, etcetera), next integrate the data in conjunction with task goals, and, at its highest level, predict future events and system states based on this understanding [Endsley, 1995a].

Situation Awareness theory primarily focuses on how visual information influences the ability of groups to formulate a common representation of the state of the task, which in turn allows them to plan and act accordingly [START_REF] Endsley | Toward a theory of situation awareness in dynamic systems[END_REF][START_REF] Endsley | A survey of situation awareness requirements in air-to-air combat fighters[END_REF]. Visual information helps team members assess the current state of the task and plan future actions [START_REF] Endsley | Toward a theory of situation awareness in dynamic systems[END_REF][START_REF] Whittaker | Things to talk about when talking about things[END_REF]. This awareness supports low-level coordination for tightly-coupled interactions.

The most commonly cited definition of SA is one suggested by [START_REF] Endsley | Toward a theory of situation awareness in dynamic systems[END_REF] who states that situational awareness "...is the perception of elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future" (p. 36, more elaborated 3-levels definition of SA is presented in [START_REF] Kulyk | Do You Know What I Know? Situational Awareness of Co-located Teams in Multidisplay Environments[END_REF]). Despite the frequency of its citation, many researchers do not accept this definition of SA. For example, [START_REF] Wickens | Workload and situation awareness: An analogy of history and implications[END_REF] suggests that SA is not limited to the contents of working memory, but it is the ability to mentally access relevant information about the evolving circumstances of the environment. [START_REF] Crane | Theories of expertise as models for understanding situation awareness[END_REF] provides a very different conceptualization of situational awareness by focusing on inadequate performance and suggests that SA is synonymous with expert-level performance.

In this research, we define situational awareness as: (1) detection and comprehension of the relevant perceptual cues and information from the environment;

(2) understanding of the situation, based on individual previous knowledge; and

(3) interpretation of these and reconfiguration of understanding and knowledge in a continuous process during the group collaboration effort. This allows awareness of changes in the environment, knowing what team members are doing and have done regarding current events in the environment, and keeping track of work progress.

Especially in multidisciplinary settings situational awareness information is affected by the abilities of individual members, their interaction with other team members, and the environment in which they collaborate [START_REF] Bolstad | Modeling shared situation awareness[END_REF]. Various factors affect individual situational awareness formation: environmental (physical location, display arrangement and size, etcetera) and group aspects (communication, use of collaboration tools, team processes, etcetera). In order to assess SA during evaluation of collaborative interfaces or awareness displays, specific factors need to be identified relevant to a particular domain. Applying an iterative user-centered design approach, we need to analyse the actual work context in order to design technology that supports team members in their primary task. Thus, this leads teams to communicate and interact in a collaborative environment with prolonged involvement and, hopefully, better results. It will also help us to find out how new technology in collaborative environments, such as large shared displays, influences daily work and team coordination [START_REF] Hallnass | From use to presence: On the expressions and aesthetics of everyday computational things[END_REF]. This and other aspects of situational awareness theory are extensively addressed in [START_REF] Kulyk | Do You Know What I Know? Situational Awareness of Co-located Teams in Multidisplay Environments[END_REF].

Based on the theory of situational awareness [Endsley, 1995a] and on the results of our previous user study and task analysis study [START_REF] Kulyk | Do You Know What I Know? Situational Awareness of Co-located Teams in Multidisplay Environments[END_REF], displaying all alternatives on a shared large display should foster information sharing and the Highlighting interface should enable group members to draw the attention of the group to a certain visualisation, while still keeping the other alternatives in view. The Highlighting-on-Demand concept supports level 1 of situational awareness, perception. The detailed description of the Highlighting-on-Demand interface, as well as results of the user evaluation are presented in section 5 of this chapter.

Social Psychology of Groups and Technology

Research in social psychology has demonstrated that our ability to make group decisions is frequently flawed because we overly rely on social cues during a group discussion [START_REF] Mcgrath | Groups: Interaction and Performance[END_REF][START_REF] Mcgrath | Groups Interacting with Technology: Ideas, Evidence, Issues and an Agenda[END_REF]. Conversations held by groups for the purpose of making decisions are fraught with complications. Social psychologists have demonstrated that individuals allow the presence of the other people in the group to influence their behavior to such a degree that through the process of exchanging opinions, the group is led to a lower quality decision, as compared to aggregating individual decisions [START_REF] Bray | Social influence by group members with minority opinions: A comparison of hollander and moscovici[END_REF][START_REF] Hackman | Group influences on individuals in organizations[END_REF][START_REF] Janis | Groupthink: Psychological studies of policy decisions and fiascos[END_REF][START_REF] Myers | The enhancement of dominant attitudes in group discussion[END_REF][START_REF] Whyte | Decision failures: Why they occur and how to prevent them[END_REF].

While groups have flawed decision processes, [START_REF] Raven | Groupthink, bay of pigs, and watergate reconsidered[END_REF] describes a wellknown experiment that aptly illustrates the difficulty in universally stating that groups hinder decision-making. The results of his experiment show that the groups that reached a unanimous decision felt more satisfied with their decision than those that did not, even if they were shown to be incorrect in their judgment. This experiment illustrates that individuals rely on the opinion of others as an indicator of the accuracy of their judgments, but this reliance can occasionally lead to an error in judgment. Yet, as a corollary to this, if a criterion of decision success is satisfaction with the outcome, then individuals' use of this decision-making strategy may be beneficial even in cases where their judgment is wrong.

With this understanding of the complexity of our limitations, what can we do to limit the harm and harness the benefits of groups? By altering its decisionmaking process, a group can avoid the above communication flaws and overreliance on others. According to [START_REF] Dimicco | Influencing group participation with a shared display[END_REF] and based on the related studies on the psychology of groups, there are three possible areas that should be examined to enhance decision-making processes with technology. First, determine ways to encourage vigilance in considering choice alternatives in the discussion [START_REF] Janis | Groupthink: Psychological studies of policy decisions and fiascos[END_REF]. Second, limit the effects of group polarisation (a group's tendency to shift towards risk or caution) [START_REF] Brown | Group polarization[END_REF]. And third, increase the sharing of information between individuals [START_REF] Stasser | Effects of information load and percentage of shared information on the dissemination of unshared information during group discussion[END_REF].

For example, vigilance can be fostered by a system that keeps all the alternative ideas in front of the group, and makes the infrequently mentioned alternatives re-appear within the discussion. Group polarization can be limited with a reframing of decisions in terms of gains, not losses, and an interface or tool that enables a group to reframe questions from different points of view. Information sharing can be encouraged within a group by allowing the documentation and presentation of individual decisions that will later be shared with the group.

Technology is already a part of daily decision-making environments (e.g., smart meeting rooms and multidisplay environments), both as communication tools and information devices. As these devices (e.g., large displays) and tools become more accessible, there is an increasing opportunity to develop applications that enhance group communication abilities, rather than restrict them. If tools can be designed such that the satisfaction with the group decision-making process increases, then the potential for achieving more gains of group interaction increases.

Objectives and Hypotheses

Based on the results of the exploratory user study and task analysis results [START_REF] Kulyk | Do You Know What I Know? Situational Awareness of Co-located Teams in Multidisplay Environments[END_REF], we have come up with a number of situational awareness (SA) concepts to explore various alternative solutions [START_REF] Kulyk | Situational awareness support to enhance teamwork in collaborative environments[END_REF] in order to support group decision making in co-located collaborative environments, pre-sented in [START_REF] Kulyk | Do You Know What I Know? Situational Awareness of Co-located Teams in Multidisplay Environments[END_REF]. One example is a Highlighting-on-Demand interface, which enables a team member who is currently controlling the shared display to draw the attention of the other team members by highlighting a certain visualisation, for example, using a slider on a shared display or a personal interaction device.

The goal of this experiment is to perform a controlled comparative case study in order to measure the effect of the Highlighting-on-Demand concept on: a) satisfaction with the final group decision and b) satisfaction with the group decision-making process in a multidisplay environment. We will assess satisfaction with the decision-making process of team members, providing supportive visualisations on a shared large display. We aim at reducing the distraction from the primary decision-making task, and increasing the group member's satisfaction, with the decision-making process and group communication, as well as satisfaction with the perceived task performance (individual decision versus group decision).

Based on theories on the formation of shared situational awareness [START_REF] Kulyk | Do You Know What I Know? Situational Awareness of Co-located Teams in Multidisplay Environments[END_REF] and social psychology of groups [START_REF] Janis | Groupthink: Psychological studies of policy decisions and fiascos[END_REF][START_REF] Brown | Group polarization[END_REF][START_REF] Stasser | Effects of information load and percentage of shared information on the dissemination of unshared information during group discussion[END_REF], displaying all alternatives on a shared large display should foster information sharing and the highlighting interface should allow group members to draw attention of the group by highlighting a certain alternative, while keeping the other alternatives still in view. Therefore, by presenting all alternatives on a shared large display and enabling highlighting on a shared touch screen, it is hypothesized that satisfaction with the group decision-making process will increase. These predictions are summarized as two hypotheses:

H1 -In the condition with the Highlighting-on-Demand interface, participants' satisfaction about group process and decision-making process will be higher.

H2 -Participants' satisfaction about the final group decision, in relation to their individual decision, will be higher in the condition with the Highlightingon-Demand interface.

Next section presents the experiment design and procedure.

Setting and Procedure

Within-group design is applied in this experiment, which means that each group of participants performs a group decision-making task in both conditions: one Without (N) and one With (Y) the Highlighting interface. The conditions are balanced (See Table 1

below). Table 1. Experiment design Group Session Condition (N/Y) Image Set (A/B) Questionnaire (see Section 4.5 below) 1 N A Part I Y B Part I & II 2 Y B Part I & II N A Part I 3 N B Part I Y A Part I & II 4 Y A Part I & II N B Part I
Legend, Table 1: Conditions: N (=NO): Without Highlighting Y (=YES): With Highlighting Image Sets: Set A = 7 various large paintings (landscape, portrait, abstract, fantasy etc.) -Figure 1. Set B = a set of 7 other various large paintings -Figure 2.

Target Group

The chosen target group for this experiment is ad-hoc small groups of four to five members. Group members are scientists who might be colleagues working together at the same faculty at the university, or in the same research group (e.g. human-computer interaction or visalisation research group), with multidisciplinary backgrounds. We mixed-up group members to create balanced small groups. As a result, some of the participants knew each other well beforehand, and others have never worked in one team together.

Group Task Scenario

In this study we address the domain of the group decision making which involves group discussion and review of the arguments prior to making a final decision. The goal of the group task in this study was to initiate a group discussion on a topic of joint interest and motivate the team members to develop an individual and a group decision-making strategy. Since it was not feasible to find several life science teams to participate in the study, we decided to choose a general task for group decision making, not related to omics experimentation or life sciences. Taking into account that ad-hoc groups consist of group members from different disciplines, we picked a general task, which would be of interest to each participant and to the whole group at the same time.

A group decision-making scenario was presented as a group task, where a group had to discuss seven paintings and then pick three of them to put it in the shared coffee room. After discussing each painting with the whole team, and the pros and cons of putting it in the shared coffee room, each group member had to pick a maximum of three favourite paintings individually. After that, participants were asked to play a ranking game, where everybody had to share their individual choice with the rest of the group by dividing 3 points between three, two or one painting. Finally, a group had to reach a decision by picking three paintings only, either by summing up the individual scores or by agreeing on the mutual group decision.

Each group had a limited time of ten minutes to reach a group decision. The main goal for the group was to reach a group decision that each group member would agree with. Each participant received a e 8 gratuity coupon for their participation. Legend, Table 2: Set 0 = a set of 7 different large paintings (Figure 3) used only for the introduction.

Instructions to the group

Your faculty at UvA has received 7 paintings as a present from students of Utrecht Art Academy. Only 3 paintings can be put in the coffee room of your research group. Your goal is to discuss these 7 paintings (presented on a large tiled display in front of you) as a group and choose 3 paintings for the coffee room of your research group. After discussing each painting (pros/cons, why you like it, why does it suit in the coffee room etc.) in a group, you will have to score them to make a group decision. Each of you will have 3 points that you can choose to either divide between 2 (e.g.: Painting 3 = 2 points, Painting 5 = 1 point) or 3 paintings, or you can give all 3 points to just one painting of your personal choice. You have to announce your group choice of favorite 3 paintings after 10 minutes. Please use a blank A4 page or a whiteboard (on your right) to put your personal and group scores.

Pilot Test

Before the actual experiment, a pilot session was conducted in order to test the procedure, the experiment design, the prototype, the position of the paintings on the shared tiled display and the displays setting. Several technical problems were found and solved during the pilot test. We only name a relevant one here:

Namely, the 'Highlight one image -fade the rest' checkbox (Figure 5, right bottom) was removed due to the distraction caused by the flashing effect on the Plasma Touch Screen. Since the Highlighting prototype was running on a Windows platform via SAGE interface, it was not possible to solve the flashing and therefore it was decided to ask the participants about the usefulness of this feature during a post group discussion and in the post-task questionnaire instead. The sliding bar (Figure 5, bottom) was left visible on the Highlighting interface. A user could highlight or fade a selected painting (or any projected window on the Tiled Display) by moving the slider left or right via direct touch or using a mimio pen on a Plasma Touch Screen (Figure 4, left). The effect was immediately visible on the Tiled Display (Figure 4, right).

Multidisplay Environment Setting

The experiment took place in the E-BioLab multidisplay meeting environment (see Figure 4). In all conditions, images were displayed on the central Tiled Display (5x4 lcd-monitors, resolution 1600x1200, 38 Megapixels) in the middle of the lab (Figure 4, right image).

In condition N (No Highlighting interface), only a moving feature (via direct touch or using a mimio pen on a 63-inch Plasma Touch Screen with a resolution of 1360x768, Figure 4, left) of the interface was shown to the participants. That means that group members were only able to move and rearrange images on the Tiled Display (Figure 4, right), using the Highlighting-on-Demand interface version without a sliding bar below (Figure 6).

In condition Y (Highlighting interface), in addition to a moving feature as in N condition, participants were able to highlight or fade paintings (via direct touch or using a mimio pen on a Plasma Touch Screen, Figure 4, left). Interaction with the Highlighting interface was logged in a text file with time stamps. All sessions were captured with four video cameras from four different angles.

Measures

Two dependent variables were measured: group process and decision making, and satisfaction. By keeping the team composition balanced and the task case constant in all groups, the effect of this variable was diminished. Likert scale questionnaires were applied to access the perceived group process quality [START_REF] Olaniran | A model of group satisfaction in computer-mediated communication and face-to-face meetings[END_REF], satisfaction with the decision-making process [START_REF] Kulyk | Realtime feeback on nonverbal behaviour to enhance social dynamics in small group meetings[END_REF][START_REF] Paul | User satisfaction with system, decision process, and outcome in gdss based meeting: An experimental investigation[END_REF], and the perceived agreement with the final group decision. All questionnaires used 5-point Likert-scale, where '1' meant 'Strongly agree' and '5' -'Strongly disagree'. Group process and decision making questionnaire was administered to team members in both N and Y condition.

Direct self-rating techniques, such as SART [START_REF] Taylor | Situational awareness rating technique (sart): The development of a tool for aircrew system design[END_REF], have a potential limitation that participants may experience difficulties rating low periods of SA [START_REF] Salmon | Situation awareness measurement: A review of applicability for c4i environments[END_REF]. In view of the rather short duration of the study, we predict that SART measure might not be sensitive enough for our case. Instead, we use perceived agreement with the final group decision as an indirect measure of shared situational awareness [START_REF] Bolstad | The effect of task load and shared displays on team situation awareness[END_REF][START_REF] Wickens | Workload and situation awareness: An analogy of history and implications[END_REF].

An additional set of questions addresses participants' subjective judgments about satisfaction with and usefulness of the Highlighting-on-Demand interface (including distraction and awareness) [START_REF] Cadiz | Designing and deploying an information awareness interface[END_REF][START_REF] Kulyk | Realtime feeback on nonverbal behaviour to enhance social dynamics in small group meetings[END_REF][START_REF] Paul | User satisfaction with system, decision process, and outcome in gdss based meeting: An experimental investigation[END_REF]. Post-task usefulness questionnaire was administered to team members only in Y condition. The overview of all measurements and techniques is given below.

-Questionnaires:

1. Satisfaction with group process and decision making (Part I) 2. Perceived agreement (Part I) 3. Usefulness and satisfaction with the Highlighting-on-Demand interface; awareness and distraction (Part II) -Observations (observation protocol) -Post-session group interviews -Video capturing (4 cameras)

Data Analysis and Results

The experiment results indicate that the use of the Highlighting interface on the shared display positively influenced team members' satisfaction with the final group decision. The main findings from the data analysis are discussed below.

Participants

In total 18 participants (15 male and 3 female, age range 25-31 years) were recruited from the university community and assigned in 4 groups (2 groups of 5 members each, and 2 groups of 4 members each). Gender was split so that 3 groups out of 4 had members of both genders. Participants were mostly students, scientific programmers and postdoc researchers from three different research groups at the same university (University of Amsterdam, UvA) and had various scientific backgrounds (e.g., computer science, physics, computational biology, mathematics, engineering). None of the participants had experience with group discussion in the multidisplay environment before.

Questionnaires

Group process, Decision Making and Situational Awareness Wilcoxon Signed Ranks test was conducted to compare group members' satisfaction with the decision-making process in Y (Highlighting -condition 2) and N (No Highlighting -condition 1) conditions. There was a significant difference in the scores on the satisfaction with the final group decision, in relation to the personal preference, for the Y-Highlighting (M5 =4.67, SD6 =0.48) and N-No Highlighting (M=4.11, SD=0.83) conditions; p7 =0.02 (z8 =-2.33). Table 3 shows the results of a Wilcoxon Signed Ranks test only for the significant result -satisfaction with the final group decision. There was no significant difference found in the scores on the satisfaction with the group process and the decision-making process (Wilcoxon Signed Ranks test). These results suggest that the Highlighting-on-Demand interface really does have an effect on the satisfaction with the final group decision. Specifically, our results suggest that when group members use the highlighting interface during the discussion, the satisfaction with the final group decision increases. In this manner, H2, stating that participants' satisfaction about the final group decision, in relation to their individual decision, will be higher in the condition with the Highlighting interface, is confirmed. On the other hand, H1, stating that in the condition with the Highlighting-on-Demand interface participants satisfaction about the group process, and the decision-making process will be higher, is not confirmed.

Perceived agreement with the final group decision is an implicit measure of shared situational awareness [START_REF] Bolstad | The effect of task load and shared displays on team situation awareness[END_REF][START_REF] Fjermestad | An analysis of communication mode in group support systems research[END_REF][START_REF] Wickens | Workload and situation awareness: An analogy of history and implications[END_REF]. This suggests that in the condition with the Highlighting-on-Demand support, the situational awareness was higher in terms of the perceived quality of group decisions and level of consensus. As the study of [START_REF] Wickens | Workload and situation awareness: An analogy of history and implications[END_REF] also indicates the ability of group members to accurately perform probabilistic diagnosis (situational awareness) coupled with the assigned values of different alternatives (ranking game), results in more satisfactory group decisions.

Usefulness, Awareness and Distraction

We balanced the valence of our satisfaction questions. For negatively phrased questions (marked with an asterisk in Table 4 andTable 5), we reversed the rating so that higher was always positive. The rating for the ability to focus the attention of others on certain information on the large display was above the average but not high enough. One of the reasons could be the difficulty to self-report the allocation of attention of oneself and others. More objective measures like eye gaze might be more efficient in this case, though there is still no eye-gaze tracking system applicable for dynamic group settings. Counting the number of times a participant attends to a certain display or a part of the display is another option, though it is burdensome and requires at least one observer per participant.

Ratings on awareness and distraction were also mostly neutral to positive (see Table 5). Awareness and distraction ratings indicate that the Highlighting interface did not distract participants from group discussion, but did not make then more aware of the information on the large display.

Ratings on usefulness and satisfaction with the Highlighting interface were mostly neutral to positive (see Table 4). From the ratings, it is clear that participants had no difficulty understanding the Highlighting interface and did not need more training to understand the interface. Group members also stated that they had confidence in the information provided by the Highlighting interface. I have difficulty understanding the highlighting interface.* 4.7 (0.5) 2.

Highlighting interface is easy to use. 3.3 (1.4) 3.

Highlighting interface is reliable. 3.0 (1.4) 4.

I have confidence in the information provided by the highlighting interface.

4.0 (0.9)

5.

I need more training to understand the highlighting interface.* 4.8 (0.4) 6.

I find the information provided by the highlighting interface informative.

3.3 (0.8)

7.

The information provided by the highlighting interface is comprehensible.

3.6 (0.8)

8.

Overall, I am satisfied with the highlighting interface.

3.1 (1.1) 9.

I would be happy to use the highlighting interface in the future.

(1.3)

User Preferences on the Highlighting Interface Features Three additional questions in the post-task questionnaire addressed the user preferences of the various features of the Highlighting interface, as well as the interaction preferences. The results indicate that 12 out of 18 participants found highlighting an image the most useful feature. Participants mentioned that the highlighting was helpful when eliminating choices: "...so that we could quickly/ efficiently move on to a decision. Visually removing eliminated options aided my group focus on the remaining choices". It was also used to emphasize, select an image, and to fade away the painting that didn't pass the vote. Among other mentioned useful features were: fading an image (7 of 18) and moving an image (4 out of 18).

Concerning the interaction preferences, 11 out of 18 participants preferred to interact with the highlighting interface via the touch screen. Tablet PC or a private PC screen was preferred by 6 out of 18 group members. Among other mentioned preferred means of interactions were, the direct manipulation on a tiled display (2 out of 18), interaction via speech (2 out of 18) and multitouch (2 out of 18). Highlighting interface helped to grab my attention at the right time. 3.0 (1.2) 3.

Highlighting interface interrupted me during the group discussion.* 4.1 (0.9) 4.

Highlighting interface helped me to be aware of information on the large display.

3.0 (1.3)

5.

Highlighting interface helped me to focus attention of others on certain information on the large display.

3.3 (1.2)

6.

I would rather have the highlighting interface displayed only privately.* 2.1 (1.0)

User Comments

Most participants had very positive responses about the highlighting interface:

-"I liked the intuitive interaction, you can directly manipulate things and everyone can see the changes right away"; -"Intuitive and fast. Would use it for spatial positioning"; -"I would like to use this for visualising data and manipulating how the data is visualised and discuss the data with others".

This first comment points to the ongoing awareness of changes in the environment. Several group members also mentioned that a feature to highlight one image and fade the rest would be useful. The wish-list features for the future highlighting interface is the multi-user and multitouch gesture interaction on the large display. Some participants complained about the black lines on the tiled display (2 out of 18), and about the reaction time of the touch display when resizing an image (1 out of 18).

Next, we discuss the qualitative results of the observations and the postsession discussions with the teams.

The Use of Large Shared Display

At the beginning of the discussion, group members would discuss each painting, why it would or would not be good to put it in the shared coffee room.

N condition: No Highlighting interface

In N condition, participants used the Plasma Touch screen less intensively. If the whole group would eliminate a certain painting, a group would ask one member interacting with the plasma screen, to move that painting below or to the side of the Tiled display. In this way, participants were free to rearrange the screen as they wished, putting the most preferred paintings, or 'to be discussed' paintings in the middle or at the top of the tiled display.

Y condition: Highlighting interface

While going through each painting, in Y condition one of the participants would highlight or enlarge9 the discussed painting. Spontaneous interactions also occurred frequently, when one group member would approach the plasma touch screen and would start interacting with the highlighting interface during the discussion, intuitively following the requests of the other team members as to which painting to highlight or to fade.

N & Y conditions

The results of our previous observations [START_REF] Kulyk | Do You Know What I Know? Situational Awareness of Co-located Teams in Multidisplay Environments[END_REF] and video analysis of scientific team discussions in the multidisplay e-BioLab environment showed that life scientists tend to walk to the shared tiled display to inspect a specific detail of a visualisation. This indicates that, unlike a static projection in the meeting room, a large shared display plays an important role in engaging the group members in the discussion. This points to the dynamic nature of interactions as reported in other studies [START_REF] Tan | Physically large displays improve performance on spatial tasks[END_REF]. Even though in this experiment only paintings were displayed and there was no need to inspect specific details of each painting during the group task, in both conditions participants still tended to gather around the display while discussing the alternatives and making the group decision. Participants tended to point at the shared large display when referring to one painting, also referring to it by a given name, such as a 'boy', or 'flowers' (Figure 2, paintings set B).

Interaction with the Highlighting-on-Demand interface

The majority of participants liked interacting with the Highlighting interface via the shared Plasma Touch Screen as it helped the other group members to follow what was being changed on the shared large screen during the discussion. Some participants also mentioned that they would also like to interact with the Highlighting interface on a private Tablet PC.

Next to using the Highlighting-on-Demand in Y condition, and moving feature in N condition, users also discovered the resizing feature during the test session as it was fairly intuitive. One of the shortcomings of the particular touch screen used in this study was the response delay when resizing an image, also caused by the fact that the SAGE user interface was originally designed for the Tablet PC. Precision was hard to achieve when resizing an image by hand on a Plasma Touch Screen. Even though resizing was not very easy, it was still used by some participants. Therefore resizing might have effected the results. Since a resizing feature was a standard SAGE interface option, and in this experiment we focus only on the Highlighting interface, we did not focus on the redesign of the SAGE interface.

Several participants who were previously very familiar with touch screens, missed the multitouch feature on the Plasma Touch Screen. On the other hand, though the Plasma Touch Screen allowed only one person to interact at a time, it helped other group members to constantly stay aware of who was in control of the shared large display during the discussion. We did not observe any conflicts between group members concerning the interaction with the Plasma Touch Screen.

Individual and Group Decision Making Strategies

Most groups used the individual ranking approach to come up with the group decision, by summing up the individual ranks after they had made their personal choice. One of the group members would usually use a whiteboard to write down the ranks, or alternatively one by one each group member would write his/her personal rank on the whiteboard. Some of the groups tended to discuss the strategy of the individual ranking for longer than 10 minutes. For instance, one very active participant in the fourth group changed the personal ranking of paintings in order to influence the final group decision in his favour.

Reflections and Future Work

The results of the observations and post-session group discussions indicate a high involvement of group members in the discussion while interacting with the highlighting interface on the shared display. This effect could be partially caused by the novelty of touch displays.

One of the shortcomings of the study is that it was hard for the participants to make a clear distinction between the Highlighting interface and the rest of the touch screen's features such as moving and resizing. Some of the participants even mentioned that, if they had not been being told clearly, they would refer to all interfaces and displays in the environment while filling in the usefulness questionnaire on the usefulness of the Highlighting interface.

Previous studies by [START_REF] Van Nimwegen | The paradox of the assisted user: guidance can be counterproductive[END_REF] and O'Hara and Payne [1999] discuss a counterproductive effect of the interfaces that present information externally on the display and fade out irrelevant information. On one hand, when information is externalized on the display (for example, by graying out momentarily unavailable buttons), users might quickly perform a problem solving task in a short run. On the other hand, the strong reliance on a visual display may have negative consequences for knowledge acquisition and task performance in the long run. We believe that giving users control over what parts of the information should or should not be externalized might be an alternative solution to the automatic information externalization. Although we did not study the longterm learning effects on the perceived performance (satisfaction with the final group decision), the results of our study suggest that providing the Highlightingon-Demand support might help to prevent the counterproductive effects named above on the group performance in problem solving tasks.

As for the role of a large display compared to just having paintings printed as large posters on the wall, we believe that interaction played an important role in our experiment. Besides the presence of a shared visual representation and the awareness of the fact that the team members can refer to the same visual representation, being able to perform simple interaction like fading, resizing and hiding the painting played a certain role in the group decision-making strategy. One of the other points on our research agenda is to study the long-term effects of shared large displays on situational awareness and decision-making strategies between co-located and distributed groups.

Summary

This chapter presented the results of the first empirical user study on the effect of the Highlighting-on-Demand concept on satisfaction with the group decisionmaking outcome in a real multidisplay environment. The Highlighting-on-Demand interface enables a team member who is currently controlling the shared display to draw the attention of the other team members by highlighting a certain visualisation by using a slider on a touch display. The results suggest that when group members use the Highlighting-on-Demand interface during the discussion, the satisfaction with the final group decision increases. Participants expressed willingness to use the Highlighting awareness support for visualising data and manipulating how the data is visualised to discuss the data with other team members in real project discussions.

Fig. 1 .

 1 Fig. 1. Paintings: set A

  (interaction via plasma touch screen) -Image Test Set 0 (Figure 3) 10 min. Task (N = No Highlighting, only moving) -Image Set A (Figure 1) 5 min. Pre-Questionnaire (Part I) 3 min. Intro & Example (Highlighting & resizing) -Image Test Set 0 (Figure 3) 10 min. Task (Y = With Highlighting & resizing) -Image Set B (Figure 2) 10 min. Post-Questionnaire (Part I & II) 5 min. Illustrate extra function: Highlight one image, fade out the rest automatically 10 min.Debriefing: post group interview

Fig. 4 .

 4 Fig. 4. Group session, Highlighting-On-Demand Experiment: group member interacting with the Highlighting interface on a plasma touch screen (left); group members discussing paintings displayed on the shared tiled display (right).

Fig. 5 .

 5 Fig. 5. Highlighting-On-Demand pilot

Fig. 6 .

 6 Fig. 6. Highlighting-On-Demand prototype: desktop version (left), image courtesy of Tijs de Kler, SARA; a touch screen version (right).

Table 2 .

 2 Session planning -group session 1 (see Table1)

	Time	duration
	(Total: 60 min.)

Table 3 .

 3 Wilcoxon signed ranks test for the differences between N (No Highlighting) and Y (Highlighting) condition

	Questions: Decision Making Process	N: Mean	Y: Mean	Sig.
		(SD)	(SD)
	Overall, I am satisfied with the final decision of	4.11 (0.83) 4.67 (0.48) 0.02
	the group, in relation to my personal preference.

Table 4 .

 4 Questionnaire results: usefulness and satisfaction

	Question

Table 5 .

 5 Questionnaire results: awareness and distraction

	Question

M -Mean

SD -Standard Deviation

p -probability value

z -critical value for a

95% confidence interval (or a 5% level of significance); a shortcut to the hypothesis testing of the Wilcoxon signed rank-test.

Despite that only the Highlighting feature was encouraged to use in the Y condition, and moving in N condition, users also discovered the resizing feature during the test session as it was fairly intuitive. Even though resizing was not very easy, it was still used by some participants.


Philipp Heim
email: philipp.heim@vis.uni-stuttgart.de

Jürgen Ziegler
email: juergen.ziegler@uni-due.de

Faceted Visual Exploration of Semantic Data

Keywords: Graph visualization, faceted search, query building, SPARQL, hierarchical facets, pivot operation

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The amount of semantically described data available on the Web has been growing considerably in recent years, turning the vision of a Semantic Web as proposed more than a decade ago by Tim Berners-Lee [START_REF] Berners-Lee | Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web by its Inventor[END_REF] more and more into reality. The growing volume of information in the Semantic Web can be seen in large information pools such as DBpedia [START_REF] Auer | DBpedia: A nucleus for a Web of open data[END_REF] or in the LOD initiative (Linked Open Data [START_REF] Bizer | Linked data on the Web[END_REF]) which aims at semantically linking a large number of different open, publicly available data sets. In these datasets information is represented by semantic structures expressed in formal languages such as RDF (Resource Description Framework) or OWL (Web Ontology Language). The structures consist of statements about real world (e.g. 'Berlin') or virtual objects referenced by a URI (Uniform Resource Identifier), an assigned ontological class, such as 'city' or 'country', and an arbitrary number of properties that define links to other objects, such as 'is capital of'. These techniques allow to search for information based on their semantic descriptions and to make data interoperable between different datasets by linking objects that have the same meaning.

The predominant view on the purposes of a Semantic Web was, and still is to enable machines to process distributed data on the Web more effectively. In recent years, however, there has been an increasing focus on the interactive use of semantic data. The Semantic Web can also help users directly to answer focused questions and to retrieve unknown factual information. In contrast to the conventional Web, however, there are a number of properties of semantic data that may impede human use unless appropriate support for searching and exploring the data is provided.

Conventional Web pages are predominantly structured and designed as coherent documents, intended and suitable for human consumption and perusal. Information units are embedded in other textual or multimedia content, providing context for clarification and a better understanding. Semantic data, on the other hand, are represented in the form of single subject-predicate-object statements and thus constitute more elementary pieces of information than standard Web pages. While the information contained in a single statement may indeed be the target of a focused search, for exploring and understanding the data, more context is usually required. The semantic relations between individual resources can be exploited to create such context. Since a pool of semantic data forms a large RDF graph, a context of interest can essentially by of arbitrary size. Users should therefore be able to define and visualize the context of some focal concept in a flexible and user-controlled fashion.

For this purpose, suitable tools are needed that support users throughout the information seeking process starting from an initial query formulation over the flexible selection of relevant context through to the identification and use of the information needed. One of the objectives of this paper is to propose a conceptual search model that is appropriate for describing the different user activities involved in searching and exploring semantic data. Since the information space to be searched can be very large, such a process typically requires the use of search and exploration activities in combination. Even if the target of the search is not well-defined at the outset, users will have to define a starting point for follow-up explorations based on some textual query. For experts, artificial query languages such as SPARQL3 are available that allow to formulate precise queries on the basis of a well defined information need. For non-experts, free text search is more appropriate for initiating the search process. In contrast to Web search, however, the result is not comprised of Web pages but can be concepts that match the query terms. In the following stages, user activities are typically more of an exploratory nature, iteratively refining or modifying the search until the desired information is found.

A promising approach for supporting exploratory search processes is faceted search [START_REF] Hearst | Finding the Flow in Web Site Search[END_REF]. In faceted search, the search space is partitioned using orthogonal conceptual dimensions based on the properties of some set of objects and their corresponding values which serve as independent filters for narrowing down a result set. Selecting a value of a facet adds this value to the query and filters the result set accordingly. Whenever the result set changes, all facets are updated to show only those attributes that can be used for further filtering (Fig. 1). Therefore, users always see only those values that can further constrain the result, but which do not result in an empty set. While faceted search was originally developed for searching relational databases, the concept can be extended to semantic data, exploiting the well-defined classes and properties of semantic data for creating facets.

In a domain such as 'football', for example, a faceted search would allow football players to be filtered by their clubs, their birthplaces, or their ages. Even so the concept of faceted search has been successfully applied in popular applications such as Apple's iTunes 4 , using it to seek information in the Semantic Web entails several new problems that are not yet sufficiently solved. Most of the problems result from the vast number of objects, classes and properties. According to [START_REF] Hausenblas | What is the size of the Semantic Web?[END_REF], the LOD cloud alone contained approximately two billion statements in 2008 already.

In this paper we introduce Facet Graphs, a new approach based on the concept of faceted search which allows the intuitive formulation of search queries for semantic data. Facet graphs are composed of set-valued nodes and labeled semantic relations between these nodes. Users can choose a node as the desired result type as well as related nodes that serve as facets for filtering the results, thereby producing a personalized search interface. The graph provides a coherent representation of multiple, even distantly connected facets on a single page, avoiding browsing and preventing users from losing track of the relationships. Each node in a facet graph contains a list of items that provides sorting, paging and scrolling functionalities and thereby enables an easy handling of even large amounts of objects. Users can build queries to filter the result list by just clicking on items in the facet nodes. Filtering is indicated by colored highlighting around the nodes affected, helping users to understand and keep track of the filtering constraints applied. Users can also at any time change their search perspective by turning results nodes into facets of related nodes.

The combination of a graph visualization with faceted search allows for an easy formulation of even complex, semantically unique search queries and thus for seeking semantically annotated information without expert knowledge.

The rest of this paper is organized as follows. First we review related work and motivate our work based on shortcomings of the existing approaches. In section 3, we propose a general information seeking model for searching and exploring semantic data. We then describe the approach of Facet Graphs and the tool gFacet in detail, and evaluate its usability in a comparative user study. In the discussion, we relate our findings to the proposed search model and analyze Facet Graphs in terms of general requirements for information search. Finally, we present a conclusion and an outlook on future work.

Related Work

Several approaches using faceted search have been reported in the literature, both for relational and semantic data. Most approaches display the facets as well as the result set as lists at different positions on the screen. Examples are tools like mSpace [START_REF] Schraefel | The evolving mSpace platform: Leveraging the Semantic Web on the trail of the memex[END_REF], Flamenco [START_REF] Hearst | Finding the Flow in Web Site Search[END_REF], Longwell [START_REF][END_REF] and Haystack [START_REF] Quan | Haystack: A Platform for Authoring End User Semantic Web Applications[END_REF]. In most cases, only facets directly connected to the result set are used. For example, football players could be filtered by facets containing the clubs they are playing for, the city they are living in or the number on their shirts. By selecting, for example, the football club 'FC Chelsea' in a 'club' facet, football players would be filtered to only those players employed by this club. Hierarchical filtering, however, that allows also indirectly connected facets to be used for query building (e.g. get only players playing for clubs in the Premier League) is not supported by these tools.

Tools like Parallax [START_REF] Huynh | Parallax and companion: Set-based browsing for the Data Web[END_REF] (Fig. 2a), Humboldt [START_REF] Kobilarov | Humboldt: Exploring Linked Data[END_REF], Tabulator [START_REF] Berners-Lee | Tabulator Redux: Browsing and writing Linked Data[END_REF] (Fig. 2b) and the Nested Faceted Browser [START_REF] Huynh | Nested Faceted Browser[END_REF], by comparison, allow for hierarchical filtering. Therefore, football players could be filtered by the cities where clubs they are playing for are located. Thus the possible options for building a query are not restricted to the direct periphery around a result set but can include dimensions that are distantly connected by other facets. Depending on the directness of their connections to the current result set, they can be integrated in a hierarchy and are therefore called hierarchical facets.

In Parallax, Flamenco and Humboldt, the hierarchy of facets is never completely visible to the user. By providing ways to browse through the facet hierarchy, users are able to include attributes of also distantly connected facets in their search query. For complex queries, it is getting more difficult for users to keep an overview of all included attributes since the corresponding facets are often scattered over several different pages.

Tools like Tabulator and the Nested Faceted Browser, on the other hand, allow the whole hierarchy to be displayed on one page. By using a tree structure to display all available hierarchical facets, users can open and close distantly connected facets in one coherent view and thus keep an overview of all attributes that are included in their queries. Since tree structures are used to depict all kinds of taxonomies in a wide range of well known applications, users need no extensive period of training to gain an understanding of how to use them.

In Tabulator and the Nested Faceted Browser, every attribute defines its own subtree that can be expanded by the user in order to see distantly connected facets and their attributes, which again can be expanded and so on (cp. Fig. 2b). So for the football players, one of the clubs they are playing for (e.g. 'FC Chelsea'), could be expanded by the user to see, for example, the city of its location (here: 'London'). Selecting 'London' would cause the list of players to be filtered to only those that are playing for 'FC Chelsea' or any other club located in this city. A combined list of all the cities that could be selected in order to filter the clubs and also the players, however, is not provided by the tree structure. The cities are partitioned in different subtrees, each for every club, that all need to be expanded in order to see all available cities. Having many subtrees opened, however, places attributes that actually belong to one facet at many different positions in the tree, leading to an increased tree structure that can possibly not be displayed on one screen. If an attribute is shared by more than one object (e.g. many clubs are located in London), it also occurs repeatedly in different subtrees.

Tree structures are well suited for visualizing and interacting with hierarchical data; however, when used for hierarchical facets, they tend to produce large and highly subdivided structures that cannot easily be overviewed by users. In this paper we therefore propose an alternative, graph-based approach to visualize and interact with hierarchical facets that aims at preventing large and confusing tree structures and hence facilitates an easy generation of semantically unique queries by the user.

A Three-Stage Model for Searching and Exploring Semantic Data

Searching and exploring linked semantic data differs in a number of aspects from conventional search paradigms as proposed, for example, in the area of informa-tion retrieval. Conventional information search is usually either completely based on free-form textual search or uses a limited well-defined set of metadata. Free text search is the main paradigm in Web search while metadata-based search is a predominant model in more structured domains such as digital library search. Semantic data, on the other hand, are more structured than free text documents but may contain an arbitrary number of semantic relations which makes it hard or, in the case of large or open corpora, even impossible to build up a complete and coherent cognitive model of the underlying structure. This latter aspects also distinguishes semantic data search from querying conventional relational databases. A further important distinction refers to the granularity of the search results. While conventional search aims at retrieving a set of relevant information ressources at the document level, the size of a single chunk of semantic data is typically much smaller, containing individual factual statements describing relations between elementary units of information.

A variety of models have been proposed for describing user behavior when searching and exploring collections of information. Classical models from information retrieval focus either on the sequential process from the formation of the search goal to the use of the results or analyse the different levels of abstraction in the search process [START_REF] Marchionini | Information seeking in electronic environments[END_REF][START_REF] Bates | Where should the person stop and the information search interface start[END_REF]. Kuhlthau, for example, describes the information seeking process from a user perspective in six stages: task initiation, selection, exploration, focus formulation, collection and presentation [START_REF] Kuhlthau | Developing a model of the library search process: cognitive and affective aspects[END_REF].

While these models have demonstrated their usefulness in general information retrieval, they do not take into account the specific properties of semantic data which can be assumed to have a significant influence on the cognitive tasks and search strategies of the user. When searching semantic data, users can make use of the explicit semantic relations which link information instances among each other as well as with the conceptual structure of the dataset expressed in terms of ontologies describing the domain(s) at hand. The search process can therefore be separated in actions selecting the concept of interest and actions that explore information at the instance level. Once a conceptual starting point for the search has been determined, the semantic relations can be used as 'guiding rails' for a systematic exploration of the environment of the initial concept. Based on these observations, we can identify a model consisting of three stages of searching and exploring semantic data which forms the conceptual basis for the gFacet tool described in the next section.

1. Goal formation and concept search: In the initial phase, users form the search goal which can be specified at very different levels of concreteness. For explorative tasks, this goal will typically be at the concept level, specifying one or more concepts that determine the initial search space the user is interested in. Since the number of concepts in a datapool or linked data is far too high to start the search with a visual exploration, this goal (for instance 'find out more about German footballers' in our running example) is translated into textual search terms (e.g. 'football' that are used for identifying a relevant start concept 'e.g. German football clubs'. This concept (or class) along with its instances, i.e. information elements of this type, constitute a point of departure for the explorative activities in the second stage. 2. Construction of the exploration space: At this stage, the user incrementally builds up a space for exploring the relevant semantic neighborhood of the starting concept. For this purpose, she can exploit the different semantic relations (object properties) linked to the starting concept. By incrementally adding relations and the instance sets (ranges) linked through these relations, users can flexibly construct subspaces of the entire dataset that are relevant to their information goal . Since these relations are independent of each other, they can be used for constructing independent facets and chains of facets. These facets can be used for providing visual tools that facilitate constructing and exploring the space. 3. Multi-perspective exploration and sensemaking: Once a relevant exploration space has been constructed by the user it can be used in various ways to explore it and to make sense of the information and relations contained in it. The semantic relations are a valuable asset to support the sensemaking process. First of all, they can help to filter the information by using related concepts as facets to narrow down the number of items of a target concept. Since semantic relations can be viewed in both directions by exchanging the roles of facet and target concepts, they support changes of perspective that are important for getting a better and more comprehensive understanding of the data and their relationships. At any time in this process, the user may choose to further expand or prune the exploration space when new information of interest is found. Stages 2 and 3 of this model are therefore closely intertwined and may be executed in an iterative fashion.

This model of searching and exploring semantic data forms the conceptual basis of the method and tool described in the next section. While other search and exploration strategies are possible and may be supported by other types of tools, the model focuses on the linking aspect of semantic data and provides a systematic method for exploring datasets the structure and content of which are largely unknown to the user. We therefore postulate that the model is particularly suited to searching open linked data due to their enormous volume and complex link structure for which users will only be able to build up partial mental models.

Visualizing Semantic Data with gFacet

In Facet Graphs, facets and result set are represented as nodes in a graph visualization (see also [START_REF] Heim | Facet Graphs: Complex Semantic Querying Made Easy[END_REF]). The semantic relations that exist between facets and result set as well as facets and other facets are represented by labeled directed edges between the nodes. Fig. 3 shows gFacet5 , a prototypical implementation of our approach of Facet Graphs. In gFacet, the node representing the result set is marked by a darker background color (cp. Fig. 3,A) to better distinguish it from the nodes representing facets. The attributes of each facet are listed within one single node, which can be scrolled, paged and sorted to handle large sets of facet values. The graph can be interactively expanded by adding nodes that serve as additional facets. Facets can be added via a drop-down list from which related concepts can be selected (e.g. 'First Bundesliga Footballers' in Fig. 3,B). Facets in the drop-down lists are ordered by the number of values. Selecting a facet (e.g. the one containing the countries where footballers are born), adds it to the graph and connects it to the existing node by a labeled edge (cp. Fig. 3,C). In this way, the user can iteratively construct a graph of facets that serves as an exploration space and fosters the user's understanding of the relationships among items.

The nodes in the graph are positioned in an aesthetically pleasing way with a force-directed layout algorithm [START_REF] Fruchterman | Graph drawing by force-directed placement[END_REF]. To prevent nodes from changing their position in the visualization too frequently, we apply a pinning mechanism that forces nodes to hold their position. When a new node is added, pinning is executed after a short time delay. This delay allows the force-directed algorithm to position new nodes in an appropriate way and at the same time prevents already existing information to change their location. Pinned nodes are indicated by a pin symbol in the upper right corner (cp. Fig. 3,E) which can also be used to control pinning manually.

The general benefits of representing hierarchical facets as nodes in a graph are:

1. The attributes for each facet are grouped into one node. 2. All nodes are shown in a coherent presentation on one page. 3. Semantic relations between the nodes are represented by labeled edges (Fig. 3,D). 4. Facets can be flexibly added and removed by the user (Fig. 3,C).

Extracting Facets and Searching for a Start Concept

To make facets selectable by the user, they first have to be extracted from the underlying data structure. In gFacet, we build SPARQL queries on the client side, send them to SPARQL endpoints using HTTP requests and extract the facets from the resulting XML data. The client-server communication as well as the graphical user interface are implemented in Adobe Flex 6 and compiled to a Flash movie, which runs in all Web browsers with Flash Player installed. An implementation of gFacet that uses DBpedia's SPARQL endpoint can be accessed online7 . Since gFacet does not need any modification on the server side, it can be used to access information from other SPARQL endpoints as well.

Extracting facets from semantic structures is performed on the basis of semantic links. The links are defined by properties like 'plays for' and connect objects such as 'Franck Ribry' with, for example, 'FC Bayern Munich'. So given a list of football players, a possible facet to filter this list would be the football clubs at least one of the players in the list are playing for. Links from facet concepts to other concepts allow to construct facets hierarchically.

Since the number of concepts or classes as well as relations in a semantic data set is typically very large and cannot be shown simultaneously, users first have to identify a start concept or ontological class which is shown as the first node in the visualization. This step is done by entering textual search terms (e.g. 'german football' in Fig. 4). While entering terms, matching classes are immediately shown in a selection list.

The corresponding SPARQL query, which returns all the classes with labels containing the words entered by the users (here: "german football"), is given in the following: SELECT DISTINCT ?class ?label COUNT(?o) AS ?numOfObj WHERE { ?class rdf:type skos:Concept . ?o skos:subject ?class . ?class rdfs:label ?label . ?label bif:contains "german and football" . FILTER (lang(?label) = "en") } ORDER BY DESC(?numOfObj) LIMIT 30

The found classes are initially ordered according to the numbers of objects contained in each class. The class selected by the user becomes the first node in Fig. 4: List of suggested classes to define the initial search space based on user input.

the graph and also the current result set (e.g. 'German football clubs'), listing all the objects of the selected class (cp. Fig. 3,A).

Based on the properties of the objects in the result set, available facets are extracted automatically and displayed in a drop-down next to the result set. Each facet in this list consists of a property (e.g. 'ground') and a class of objects this property leads to (e.g. 'Football venues in Germany'). It is thus possible to have several facets with the same property but different target classes, or with different properties but the same target class. The corresponding query looks as follows:

SELECT DISTINCT ?prop ?newClass COUNT(DISTINCT ?objNewClass) AS ?numOfObj WHERE { ?objCurrClass skos:subject <URIofGermanFootbalClubs> . ?objCurrClass ?prop ?objNewClass . ?objNewClass skos:subject ?newClass . ?newClass rdf:type skos:Concept .} ORDER BY DESC(?objNewClass) ?prop ?newClass LIMIT 40

Similarly to adding first order facets to the result set, also facets of second or higher order can be added to the graph and used for filtering. In higher order facets, only those objects are shown that are connected to objects in other visible facets that are in turn directly or indirectly connected to objects in the result set. Fig. 5 shows a result set on the left, a first order facet in the middle and a second order facet on the right together with a schematic representation of the visible (black) and invisible (gray) objects. The gray dots in the second order facet represent objects of that class which are not connected to any object in the result set and therefore not visible. In this way, all the facets in the graph only contain objects that can be used for filtering but will never result in an empty result set.

Exploring the Search Space

After selecting at least one facet node in addition to the result node, it can be used to build semantically unique search queries. By selecting one of the objects in a facet, the result set is filtered to only objects that are directly or indirectly connected to the selected one. For example, the selection of 'Germany national football team' in the second order facet in Fig. 5 filters the football clubs to only those with players playing for this national team (see the new result set in Fig. 6). Fig. 6: Hierarchical facets can be used for filtering.

To support traceability and sensemaking, we color-code the border of each node according to the current filters in effect for this node. If a facet object is selected, the selected object itself, all the filtered nodes and all the edges between them are marked with the same color (Fig. 6). When several filters are in effect As shown in Fig. 7, filtered nodes are surrounded by colored borders for each selection that affects them. New borders are added to a node like tree-rings when new filters come into effect. Deselecting the facet object that constrains the result also removes the corresponding border. In the current implementation, multiple filter selections represent a logical 'AND', constraining the result set to only those objects that are connected to all selections. Other logical operations would be possible, but have not yet been investigated for the current system.

Pivot Operation

While adding facets to the graph or filtering the result set in various ways, users may at any time change their minds about what they are interested in. gFacet supports such situated, exploratory behavior. When, for example, searching for 'German football clubs', users could become more interested in 'First Bundesliga footballers' and would like to explore which clubs they are playing for. They therefore might want to use clubs as a filter for footballers instead of the other way round. This operation is supported by a pivot operation which allows to change the perspective and direction of the filtering process. The pivot operation is known from the field of data drilling [START_REF] Gray | Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub-totals[END_REF], where data are represented according to different dimensions.

The Nested Faceted Browser [START_REF] Huynh | Nested Faceted Browser[END_REF] and Humboldt [START_REF] Kobilarov | Humboldt: Exploring Linked Data[END_REF] are the only tools mentioned in the related work section that allow users to perform a pivot operation and thus to change the focus of their search. In Humboldt, the user can replace the current result set displayed in the centre of the screen, by one of its visible facets, which are arranged on the right-hand side of the display. The chosen facet becomes the new result set and vice versa, the replaced result set becomes a new facet. In Humboldt, only directly related facets are shown next to the result set and thus hierarchical facets can only be reached by operating pivots. Whenever a pivot is operated and a new result set is shown in the centre of the screen, the list of directly related facets is updated accordingly. This way, information is partitioned over multiple pages, placing substantial cognitive load on the users to keep track of former result sets and facets, which are not visible yet.

Our graph-based approach, by contrast, allows a pivot operation to be performed without any changes of the displayed information structure and thus reduces the cognitive load to keep track of them. Clicking the 'RS'-Button (RS = result set) next to the pinning needle of any of the turns this facet into the new result set and vice versa the current result set into a new facet. Whereas other approaches have to rebuild their complete layout to keep the displayed facets up to date, in our approach even distantly related facets will keep their position while operating pivots. The only aspect that changes in gFacet when operating a pivot is the number of objects in both the result set and the visible facets.

User study

To evaluate the usability of our approach, we conducted an initial user study that compared gFacet with Parallax [START_REF] Huynh | Parallax and companion: Set-based browsing for the Data Web[END_REF], a tool that also uses a facetted approach to searching semantic data but which is not based on a graph visualization. For both tools we measured to what extent participants were able to solve the following three task types of different levels of difficulty:

1. Find two players who are playing for a certain club. 2. Find two cities where players who are playing for a certain club are born. 3. Find one player who is playing for a certain club and is born in a certain city.

We applied a 2x3 (tool type x task type) within-subject design. To control for learning effects, each participant was assigned to one of two groups which used the two tools in reversed order.. After completing all three tasks with one tool, the participants were asked to fill out an evaluation sheet to rate this tool. In a final questionnaire, participants had to directly compare both tool types with each other.

Ten participants took part in the study, with an average age of 28.3 (ranging from 24 to 31). Eight of them were male; two were female with all ten participants having normal or corrected to normal vision and no color blindness. Education level of the participants was at least general qualification for university entrance and they were all familiar with computers. The functionalities of both, gFacet and Parallax were introduced by videos in which sample tasks were solved.

Results

Overall, gFacet performed very well for complex tasks. However, it performed less well for more simple tasks that could also be accomplished by just following links. The bar chart in Fig. 8a shows the number of correct solutions for each of the three different task types using the two different tool types. For task 1 and task 2, Parallax performed equally or even better than gFacet. This was mainly because in Parallax participants could accomplish both tasks just by following links and did not have to filter at all. In gFacet, by contrast, they had to filter despite the simplicity of the first two tasks and hence did not accomplish task 2 in four cases. The unfamiliar approach of graph-based facets seemed partly too complex to be properly used in average exploratory tasks. However, in order to find the right solution for more complex tasks, as for example for task 3, gFacet performed significantly better than Parallax. It was almost impossible so to find the right solution for task 3 just by following links in Parallax. All the links had to be checked in a trial-and-error method to actually find the player that is born in the given city. Since this is quite time-consuming, participants were forced to apply filters in Parallax as well.

To find the right solution to task 3, participants had to filter the list of all football players to only players that are playing for a certain club and are born in a certain city. Even though Parallax generally supports applying multiple filters, participants felt uncertain about the solutions they found. A reason for this is the strict separation in Parallax between exploration, which can be realized by clicking links on the right side of the content, and filtering, which can be done by clicking links on the left side (cp. Fig. 2a). If users explore a list of objects (e.g. cities where players are born) these objects cannot be used for filtering.

On the other hand, if users look at the filter options on the left side, they cannot further explore these options for hierarchical filters. To filter hierarchically, users first have to explore objects via links on the right side (e.g. cities where players are born) and then have to filter this list via links on the left side (e.g. to only cities located in Germany). Having to change from one side to the other can confuse users and thus can decrease their confidence in the found solutions. Moreover, clicking links to explore objects completely replaces the current content as well as the links at both sides. That way information gets partitioned over multiple pages and thus hampers users' understanding of the relations that exist between information (cp. bar chart in Fig. 8b). In gFacet, by contrast, all objects in all lists can be used for both filtering and the exploration of further facets and are all visible on one page.

Comparison of Tools Based on the Information Seeking Process

To discuss the advantages and limitations of our approach and to compare it with existing techniques, we use the general information seeking process (ISP ) as described in [START_REF] Kuhlthau | Developing a model of the library search process: cognitive and affective aspects[END_REF] and determine to what extent existing tools support the different activities in the process. The ISP can be described in six stages: task initiation, selection, exploration, focus formulation, collection and presentation [START_REF] Kuhlthau | Developing a model of the library search process: cognitive and affective aspects[END_REF].. In the following, we propose a number of concrete requirements for each of these stages and analyze whether the tools fulfill the requirements (Fig. 9). The first stage of the ISP, the task initiation, begins with awareness of lack of knowledge and leads to a concrete definition of the problem and its relation to prior experience and knowledge. Requirements are: A continuous support of the whole ISP including the problem definition (R1.1) and, based on this definition, suggestions on how to address the information need considering previously operated ISPs (R1.2).

The next stage, the selection, includes tasks like the identification and selection of the general topic to be investigated. Requirements are: An overview of all available topics (R2.1), for instance in form of a map that can be zoomed in and out, together with automatic suggestions of topics (R2.2) based on the entered problem description, entered keywords or an auto complete functionality. Both requirements aim at lowering the barrier to start an ISP.

The selection is followed by the exploration stage. It includes the investigation of the general topic, locating information and relating it to what is already known. Requirements are: A graphical representation of information that can be understood by the user (R3.1), interaction possibilities that are self-explanatory and easy to use (R3.2), the accessibility of details on demand (R3.3), sorting and Fig. 9: Comparison of existing systems with respect to requirements supporting a six-stage information seeking process. paging techniques to handle large datasets (R3.4) and zooming functionalities that are capable of showing information in different levels of detail (R3.5).

The focus formulation includes tasks like the identification and the selection of hypotheses that result in the formulation of certain filters and thus allows a focused perspective of the topic. It is rather an iterative process than one that is strictly linear. Requirements are: The interactive and intuitive formulation and change of filters (R4.1), their immediate execution on the data (R4.2), the combination of different filters (R4.3), the possibility to build hierarchical filters (R4.4), the traceability of effects caused by each of them (R4.5) and a possibility to change the focus (R4.6).

After the focus formulation, the collection takes place. Tasks are to gather and select information related to the focused topic. Requirements are: Easy mechanisms to select interesting findings (R5.1) and to export the selected information for further use in other systems (R5.2).

The last stage of the ISP, the presentation, consists of the task to present the found information. Requirements are: A broad range of opportunities to visualize the findings (R6.1).

From the pattern shown in Fig. 9 it becomes obvious that none of the tools supports the initial and the final stages of the ISP. gFacet apparently fulfills more requirements than any other tool. However, R3.5 is also not fulfilled by any of the tools. This is potentially problematic for the Facet Graph approach since extending the amount of information shown in the graph would result in very large images that are hard to overlook and handle. Introducing appropriate zooming techniques along with a better support for the initial and final stages are therefore possible extensions of the tool.

Conclusion and Future Work

In this paper we described Facet Graphs, a new approach for building semantically unique queries based on the concept of faceted search in combination with graph visualization. In addition to the general advantages of faceted search, the visualization of facets as nodes in a graph allows the direct representation of relationships between nodes by labeled edges and thus a connected presentation of the result set together with all relevant facets on one page. The user can add and remove facets to the graph to produce a personalized interface including even distantly connected and multiple facets that can be used to filter the result set from different user-defined perspectives. All caused filtering effects are color-coded in the graph making them better understandable and traceable for users.

We introduced gFacet, a prototypical implementation of our approach that can query arbitrary SPARQL endpoints (e.g. DBpedia) to access information according to its semantics. We conducted a user study to compare gFacet with Parallax and found out that our tool is especially applicable in scenarios where multiple aspects from different domains need to be integrated in order to find certain information. Such scenarios seem to be particularly interesting for querying the Semantic Web because of its huge variety of domains with each containing large amounts of different classes, objects and properties. This opens up new and outstanding opportunities for users to access information; however, controlling such powerful opportunities remains a challenging task.

Future work includes the integration of appropriate zooming functionalities in combination with a focus and context technique to foster users to retain an overview even when using massive amounts of facets in one graph. Another interesting idea is to provide an opportunity to save especially helpful combinations of facets and share such search interfaces with other users. This would further lower the barrier of acceptance for using gFacet since users can load existing search interfaces that are built by more experienced users and thus do not need to start from scratch.

Fig. 1 :

 1 Fig. 1: In faceted search, selecting a value in a facet (1) filters the result set (2), with subsequent updating of the other facets (3).

Fig. 2 :

 2 Fig. 2: a) Screenshots of Parallax and b) Tabulator.

Fig. 3 :

 3 Fig. 3: In gFacet, the result set (A) and the facets are represented by nodes and connected by labeled edges (D). By selecting properties out of drop-down-lists (B), further facets can be added as new nodes to the graph (C).

Fig. 5 :

 5 Fig. 5: Only objects that are connected to the result set are visible in the hierarchical facets: Here the national teams for which players of German clubs are playing.

Fig. 7 :

 7 Fig. 7: Multiple selections.

Fig. 8 :

 8 Fig. 8: a) Number of right solutions to the given tasks and b) comments to the statements 'It was difficult to understand the relations between the information'.

http://www.w3.org/TR/rdf-sparql-query/

http://www.apple.com/de/itunes/

A description of an early version of this prototype with limited functionalities and access to dummy data only can be found in[START_REF] Heim | gFacet: A Browser for the Web of Data[END_REF].

http://www.adobe.com/products/flex

http://gfacet.semanticweb.org.