The Advanced Relation Model for Program Visualization (ARM 4 PV) proposed in [1] is a two-phased decoupled model. In the first phase, information about programs are obtained and represented as a set of connected relations organized into a hierarchy. In the second phase, various visualization sub-models are applied over the relational hierarchy abstraction. A front-end software renders the output from the visualization models; currently, AVS/Express is used. The visualization component provides a montage-view of relations and relation attributes that, collectively, describe the semantics of the program and associated properties. Both program-space and execution performance information can be represented and visualized. The intent is to facilitate better comprehension and understanding of the program and its operations. Preliminary results are reported in [1, 2, 3, 4].

The Advanced Relation Model for Genome Sequence Visualization (ARM 4 GSV) is a recent adaptation of the ARM 4 PV. Here, the first phase of the model is adapted to identify and extract relevant information from an RNA or DNA molecule and formulate that information into a relational hierarchy. The second phase of the model is then applied over this new hierarchy. Visualizations of codon distribution and close inversion (palindrome) distribution are recently published [5,6].

At present, there are four visualization sub-models defined within the ARM 4 PV and the ARM 4 GSV: the Program-Scientific Visualization (PSV) model, the Conceptual Crown Visualization (CCV) model, the Geometric Representation of Programs (GRP) model and the Parameterized Iconic Glyph (PIG) model. Additional models may be developed. The PSV model describes a methodology that allows data visualization techniques to be applicable to information visualizations; illustrations appear in [1, 2, 3]The CCV model displays the relational hierarchy information as a 2-D graph rendered as a 3-D circular plot: examples are shown in [1, 2, 3, 5,6]. The GRP model, informally proposed in [7, 8], represents computations as points in a metric space and relations are mapped to the edges between the points. The PIG model combines the aspects of icons and glyphs to represent functional information about data set.