The Past and Future of Linked Views in Scientific Visualization

Goodman, Alyssa

In the late 1960's and early 1970's the statistician John Tukey and his colleagues carried out seminal work on "Exploratory Data Analysis," which relied heavily on what I shall call "linked views" of data. In essence, linked views are those where selection of a sub-set of data in one view (e.g. lassoing a region on a map of points observed) effects the display of different dimensions of the same data in a different view (e.g. highlight all the observations in the map-lassoed region on a graph of temperature versus density). The best uses of linked views make the links happen in real time, and also offer algorithmic as well as graphical options for subset selection.

While a growing need for systems implementing linked views--thanks to expanding data set volumes--were clear twenty years ago (see Buja et al. 1991 and references therein) such systems were not widely implemented. In the commercial world, a notable implementation of linked views came with the advent of DataDesk which began (in 1986) as a Mac-only program, but DataDesk missed the opportunity for widespread adoption thanks to the dominance of Windows. In Astronomy, the VO-compatible program TOPCAT is one of the only current tools that implements linked views effectively. Not only can TOPCAT interlink points within its own display windows, it can also "live link" to other programs via use of the SAMP messaging protocol which can connect catalog statistical (ASCII-based) tools like TOPCAT to image-based applications such as ds9, GAIA, Aladin, and WorldWide Telescope and spectral tools like SPLAT.

In this talk, I will explain and demonstrate live why the future of linked views must address two currently unsolved issues. First, in many fields, including notably astronomy and medical imaging, "maps" have become three dimensional, so a two-dimensional "lasso-like" approach fails. We need three-dimensional selection tools. And second, while protocols like SAMP make linked views possible (at least within astronomy), they do not address the user-interface (too many windows, confusion, lack of screen real-estate) issues that arise when large, diverse, multi-dimensional data sets are to explored.

I shall conclude by urging the ADASS community take the lead within science on effectively interlinking views offered by modern tools, so that Astronomers' near-term options for exploratory data analysis can once again be a future-leaning example for other scientists.

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