Clustered graphs are graphs with recursive clustering structures over the vertices. This type of structure appears in many systems. Examples include CASE tools, management information systems, VLSI design tools, and reverse engineering systems. Existing layout algorithms represent the clustering structure as recursively nested regions in the plane. However, as the structure becomes more and more complex, two dimensional plane representations tend to be insufficient. In this paper, firstly, we describe some two dimensional plane drawing algorithms for clustered graphs; then we show how to extend two dimensional plane drawings to three dimensional multilevel drawings. We consider two conventions: straight-line convex drawings and orthogonal rectangular drawings; and we show some examples. 1 Introduction Graph drawing algorithms are widely used in graphical user interfaces of software systems. As the amount of information that we want to visualize becomes larger, we need more structure ...

We seek to take some initial steps towards a theory of information that is adequate for understanding and designing systems that process information, i.e., information systems in a broad sense. Formal representations of information are needed in designing, using and maintaining such systems, especially when they are computer based. However, it is also necessary to take account of social context, including how information is produced and used, not merely how it is represented; that is, we need a social theory of information. Ideas from ethnomethodology and semiotics, as well as logic and the sociology of science, are used to explore the nature of information.

This paper discusses the relationship between formal, context insensitive information, and informal, situated information, in the context of Requirements Engineering; these opposite but complementary aspects of information are called "the dry" and "the wet." Formal information occurs in the syntactic representations used in computer-based systems. Informal situated information arises in social interaction, for example, between users and managers, as well as in their interactions with systems analysts. Thus, Requirements Engineering has a strong practical need to reconcile the dry and the wet. Following some background on the culture of Computing Science, the paper describes some projects in the Centre for Requirements and Foundations at Oxford. One of these is a taxonomy for Requirements Engineering methods. Another is applying techniques from sociology and sociolinguistics to requirements elicitation, and in particular, to determining the value system of an organisation. These pro...

In this paper, we introduce a new graph model known as clustered graphs, i.e. graphs with recursive clustering structures. This graph model has many applications in informational and mathematical sciences. In particular, we study C-planarity of clustered graphs. Given a clustered graph, the C-planarity testing problem is to determine whether the clustered graph can be drawn without edge crossings, or edge-region crossings. In this paper, we present efficient algorithms for testing C-planarity and finding C-planar embeddings of clustered graphs. 1 Introduction Representing information visually, or by drawing graphs can greatly improve the effectiveness of user interfaces in many relational information systems [12, 17, 18, 5]. Developing algorithms for drawing graphs automatically and efficiently has become the interest of research for many computer scientists. Research in this area has been very active for the last decade. A recent survey citelabel13new of literature in this area inclu...

Clustered graphs are graphs with recursive clustering structures over the vertices. For graphical representation, the clustering structure is represented by a simple region that contains the drawing of all the vertices which belong to that cluster. In this paper, we present an algorithm which produces planar drawings of clustered graphs in a convention known as orthogonal-grid rectangular cluster drawings. The drawing produced by the algorithm has constant number of bends on each edge and has O(n 2 ) area, which is as good as existing results for classical graph drawings. 1 Introduction Clustered graphs are graphs with recursive clustering structures over the vertices (see Fig. 1). This type of clustering structure appears in many systems. Examples include CASE tools [19], management information systems [10], and VLSI design tools [8]. For graphical representation, the clustering structure is represented by a simple region that contains the drawing of all the vertices which ...

Graphs are often used to represent relational information. As the amount of information that we want to visualize becomes larger and more complicated, classical graph model tends to be insufficient. In this paper, we introduce and show how to draw a practical and simple graph structure known as clustered graphs. We present an algorithm which produces planar, straight-line, convex drawings of clustered graphs in O(n 2:5 ) time. We also demonstrate an area lower bound and an angle upper bound for straight-line convex drawings of C-planar graphs. We show that such drawings require\Omega\Gammaq n ) area and the smallest angle is O(1=n). Our bounds are unlike the area and angle bounds of classical graph drawing conventions in which area bound is \Omega\Gamma n 2 ) and angle bounds are functions of the maximum degree of the graph. Our results indicate important tradeoffs between line straightness and area, and between region convexity and area. 1 Introduction Many systems, particular...

Clustered graphs are graphs with recursive clustering structures over the vertices. For graphical representation, the clustering structure is represented by a simple region that contains the drawing of all the vertices which belong to that cluster. In this paper, we present an algorithm which produces planar drawings of clustered graphs in a convention known as orthogonal grid rectangular cluster drawings. We present an algorithm which produces such drawings with O(n 2 ) area and with at most 3 bends in each edge. This result is as good as existing results for classical planar graphs. Further, we show that the bend performance of our algorithm is optimal. (Extended Abstract) 1 Introduction Clustered graphs are graphs with recursive clustering structures over the vertices (see Figure 1). This type of clustering structure appears in many systems. Examples include CASE tools [40], management information systems [19], and VLSI design tools [15]. For graphical representation, the clust...