Merging Language, Direct Manipulation, and Visualization: A Programmable Research Environment for Diffusion- Limited Aggregation

Keywords:

Introduction

Successful end-user programming environments have evaded some of the cognitive barriers to programming, with spreadsheets and computer-aided-design systems being the classic examples. We contend that computational scientists can benefit from the application of techniques discussed in [4] and [5] to their tasks.

Some of the simulation models store the cluster in a tree structure, and the analysis procedures can be quite sophisticated. One researcher whom we interviewed had written over ten thousand lines of analysis code. The same researcher had implemented his own three-byte integers to save memory. Clearly, DLA research involves writing real programs.

Since clusters grow in space over time, spatial and temporal information are fundamental to DLA research. Papers published in the field almost invariably include pictures of DLA aggregates, and many of the cluster characteristics are most naturally described in visual terms. The growth of a cluster over time is shown either by a color scheme or by successive pictures.

Our literature search and interviews indicate that the vast majority of DLA research takes place non-interactively, and that visual representations are typically static. Given the exploratory nature of the research, interaction and visualization would seem to be clear benefits.

A PROGRAMMABLE RESEARCH ENVIRONMENT

PREDLA is being written in MIT Scheme [1], a dialect of LISP. Although all of PREDLA's functionality is available through the Scheme interpreter, the most common interactions (such as running simulations, displaying and analyzing clusters and parts of clusters) will be supported by direct manipulation. PREDLA will also support the representation of arbitrary functions as combinations of graphics and text which can be called by direct manipulation.

Since research paths are difficult to anticipate, there will be times when a researcher will need to add functionality that PREDLA does not provide, such as a new simulation model, or a new analysis technique. PREDLA's design supports this process, too. PREDLA will not be a monolithic application, but consists of a carefully constructed set of language primitives and higher-level tools built from these primitives. The intent is that a researcher will be able to use and/or modify existing higher-level functions and data structures rather than starting from raw Scheme.

GOALS

We expect to see several higher-level results in the context of PREDLA. First, the direct-manipulation tools should reduce the cognitive load for the researcher, by keeping important features of the simulation and analysis visible. Second, the organization of the language primitives should facilitate the production of new functionality without sacrificing the fine control frequently required by programmers. Third, the availability of a large number of different tools in PREDLA, coupled with the immediacy of visualization and direct manipulation, may contribute to a richness of interaction not found in conventional computational science environments.

Finally, we wish to determine whether the lessons learned from PREDLA have any utility in other computational science domains, or to the higher-level issue of programming in general.

EVALUATION

Although few DLA researchers are immediately available to us, we are actively working to have DLA researchers use PREDLA, and we hope that some will use it for an extended time. Since PREDLA is a research environment, not a walk-up-and-use system, longer-term evaluation is likely to produce a more accurate portrait of PREDLA's strengths and weaknesses.

IMPLICATIONS

Acknowledgments

FOOTNOTES