5 Challenges and Unsolved Problems

data [76]. Parallel sets can be considered an extension of parallel coordinates. This visualization shows the relationships between different questions in the survey. Image courtesy of H. Hauser. Fig. 5.8. Time Histograms are able to visualize time-dependent data in a still image [461]. Time is given a spatial dimension along one histogram axis. With categorical data the problem becomes even worse. If each category is treated as a data dimension, then it’s possible to have hundreds of dimensions. An example is described by Bendix et al. [76] who apply parallel sets–an extension of parallel coordinates–to an application with 99 dimensions (Figure 5.7). The case stems from a questionnaire containing information from about 94,000 households attempting to assess living standards. A particularly difficult challenge stems from the objective of trying to understand the relationships between multiple attributes (or dimensions) in the data. Although time can be considered as another data dimension or attribute, it is treated separately here since time normally adds motion to a visualization. Effective, time-dependent visualization techniques promise to remain a future research challenge for several years to come. Watching objects in motion gener5 Challenges and Unsolved Problems 247 Fig. 5.9. Multiple, linked views are used in combination with brushing (middle) in order to filter out data in areas of interest (left) [205]. On the left is the scientific (or geometric view) of the data while the scatter plot view is on the right. Here, CFD simulation data is being analyzed. Image courtesy of H. Doleisch et al. ally provides more insight than static images, but also requires more cognition on behalf of the viewer. The transient nature of a dynamic visualization can make some things not only easier to see, but also more difficult to see, e.g., fast moving phenomena. Also, representing motion in a static image generated from a time varying data set can be very challenging and relatively few methods have been presented on this topic [461] (Figure 5.8). One of the fundamental challenges with representing time in a static image lies in the length of time to be shown–both in the past and in the future. Ultimately, the needs of the user will play a large role in deciding this. Challenge #10: Data Filtering. As mentioned in our top future research challenge in regards to assessing data quality: not all data is equal. Not only is not all data of equal quality but not all data is of equal interest or importance. Most would agree that one of the central problems of the current digital age and perhaps even of the twenty first century centers around the fact that we have too much information. In a 2003 study lead by P. Lyman and H.R. Varian entitled “How Much Information”, it is estimated that five exabytes (5 × 10 bytes) of data were produced world wide. And the rate of storage is growing each year at a rate of more than 30%. Consequently, developing tools that filter the data, namely, techniques that separate the data into interesting and uninteresting subsets is one of the major research challenges of the future (Figure 5.9). As an example, consider the AT&T long-distance telephone network. AT&T maintains a database of all calls made using this network for a time period of one year [410]. The network connects 250 million telephones from which hundreds of millions of calls are made each day. Analyzing and visualizing this data in order to find fraudulent phone calls is a serious undertaking. Developing visualization tools to filter out the important information from such data sets is challenging for at least two reasons. Firstly, the size of the data set makes searching more difficult and time-consuming. Secondly, filtering the data based on importance 2 Available at: http://www.sims.berkely.edu/how-much-info 248 R.S. Laramee and R. Kosara or interest measures is a function of the user. Different users will filter the data based on different criteria. In fact, one could view the new field of visual analytics from a pure visual filtering point of view [826]. The goal of visual analytics tools is to separate interesting data from non-interesting data. Visual analytics tools allow users to interactively search data sources for features of interest, special patterns, and unusual activity. In scientific visualization, such filtering is often called feature extraction [660] or feature detection [409] (challenge number 11) and time-dependent feature extraction is referred to as feature tracking. A typical example of feature extraction can be found in flow visualization. Various algorithms have been developed to extract vortices from vector fields either automatically or semi-automatically. Another approach is to interactively extract features of interest using a combination of multiple, linked information and scientific visualization views [206] (Figure 5.9). Regardless of the terminology used, software that helps a practitioner search and find those subsets of the data deemed most interesting will be in very high demand in the future. And visualization software is particularly suited for this challenge because it takes advantage of the high bandwidth channel between our visual and cognitive systems. Challenge #11: Cross-Platform Visualization. This problem is identified multiple times previously [349, 409] and described in detail in Thomas and Cook [826] in the section on “Collaborative Visual Analytics”. Two users rarely have the exact same set of hardware. If we consider both the hardware and the software configurations of a user, the probability of an exact match is highly unlikely. For a long time, advances in display technology were fairly slow. However, flat panel display technology has made rapid advances in recent years. The cost of display technology has also fallen, making display technology virtually ubiquitous in many countries. If we consider the range of possible hardware configurations: from desktops and laptop computers with various combinations of graphic cards and monitors, to handheld devices like cell phones, PDAs, and other electronic hand-held devices, to large displays using digital projectors, and we throw in various operating systems and memory resources for each of those devices then we are left with a vast array of possible hardware and software combinations. And the range of different possibilities is expanding, yet each user will demand advanced visualization functionality. Consequently, visualization tools that are able to cross inter-platform bridges will remain a serious challenge in the future just from a technical point of view (and also from a human-centered point of view as mentioned in the challenge concerning collaborative visualization). Currently, we are witnessing an explosion in research literature related to the topic of programmable graphic card capabilities [239]. Many visualization algorithms have been written that are tied to an individual graphics card and the set of programming language capabilities that it supports. We see a rather negative aspect of this trend and we are not in full support of this as a research direction. In fact, this trend works against the goal of cross-platform visualiza5 Challenges and Unsolved Problems 249 data user visualization V D K P