Envisioning a Path to the Future

Visualizing Patterns and Trends in Scientific Literature – What’s next? Chaomei Chen

Many of us are interested in visualizing patterns and trends in scientific literature. It can be very exciting and revealing as well as challenging and frustrating. More often than not, a visualized ‘big picture’ of a scientific field invites more questions and more specific needs. Some may want to see more details; others may prefer a birds-eye view.

There are quite a few unanswered questions. I’d like to line up a couple of them here. First of all, given any visualization of scientific literature, who would be able to understand what it is about? If there is such a thing as a typical viewer, what would be the viewer’s knowledge structure? The intended audience of the graphical message carried by the Pioneer spacecraft was aliens who would have competent knowledge of physics, at least as the way we understand it. If designers do not spell out their intent, where are the clues?

The second question may help us to narrow down the answers to the first one. How would seeing an algorithmically visualized world change us? We could become somewhat wiser, somewhat more knowledgeable, or even somewhat more confused. Some changes could be profound and intriguing, whereas some could be superficial and transient. Given the holistic view of science as a whole, how do we measure the short-term as well as long-term impact size of such a revelation?

The third question is about the value of a visualization artifact. Is a naturalistic visualization more valuable than a filtered and synthesized one? Is a prescriptive visualization more desirable than a descriptive one? Is there a non-visual alternative that could bring us straight to the point? In the long run, do we expect to change the way we are thinking, with or without abstract roadmaps of scientific literature?

I’d like to invite you to experience a particular type of visualization – knowledge domain visualization – in CiteSpace. CiteSpace is a Java application that takes bibliographic data retrieved from the Web of Science and visualizes the salient structural and temporal patterns in networks of co-cited articles. The goal is to help us to find out landmarks in a field and how these landmarks are connected. The assumption is that these patterns can help us to get a grip on the dynamics of a scientific field at a macroscopic level. CiteSpace is freely available to anyone, along with a quick user guide. Visit: http://cluster.cis.drexel.edu/~cchen/citespace

The image below is generated by CiteSpace, showing a filtered network of co-cited articles in social network analysis. Can you guess what it is telling?

(Figure 1, click to enlarge). A filtered and enhanced network of 721 co-cited articles in social network analysis. The colors are time coded from 1993 (blue) to 2004 (red).

Chaomei Chen, Editor in Chief, Information Visualization
College of Information Science and Technology, Drexel University
Email: chaomei.chen@cis.drexel.edu; http://www.pages.drexel.edu/~cc345

The Thrill of Discovery: Information Visualization as a Telescope for High Dimensions - Ben Shneiderman, HCIL

Interactive information visualization provides researchers with remarkable tools for discovery. By combining powerful data mining methods with user-controlled interfaces, users are beginning to benefit from these potent telescopes for high-dimensional spaces. They can begin with an overview, zoom in on areas of interest, filter out unwanted items, and then click for details-on-demand. With careful design and efficient algorithms, the dynamic queries approach to data exploration can provide 100msec updates even for million-record databases.

Researchers respond by polishing their designs, conducting more realistic evaluations, and developing integrated solutions that start from data gathering and go to dissemination of insights. Developers know that they must tune their applications to specific professional domains such as gene expression analysis, financial data, or terror threat assessments. They are also looking for broader commercial applications such as ebay auctions, airline reservations, or consumer product shopping.

Our recent research includes:

1) TimeSearcher for visual exploration of large time series data in auctions, meteorology, and oil/gas discovery (www.cs.umd.edu/hcil/timesearcher).

2) Hierarchical Clustering Explorer 3.0 that now includes the rank-by-feature framework (www.cs.umd.edu/hcil/hce). By judiciously choosing from appropriate ranking criteria for low-dimensional axis-parallel projections, users can locate desired features of higher dimensional spaces.

3) Treemap 4.0 for exploring hierarchical data sets such as the gene ontology, digital libraries, public health data, and and portfolio management (www.cs.umd.edu/hcil/treemap)

The growing commercial success stories such as www.spotfire.com, www.smartmoney.com/marketmap and www.hivegroup.com reflect a gradually shifting popular acceptance of these novel approaches. Some users love these visualizations on first sight and respond with enthusiasm. Others take longer to grasp what they are seeing, and more importantly to realize how they might apply such tools to their data. They get hooked when they realize that information visualizations often present them with answers to questions that they didn't even have: outliers stand out, trends become apparent, clusters make sense, and gaps invite attention.

Ben Shneiderman ben@cs.umd.edu
Founding Director, Human Computer Interaction Lab
Dept of Computer Science, University of Maryland
College Park, MD 20742 www.cs.umd.edu/~ben