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Growing Squares: Animated Visualization of Causal Relations
, 2003
"... We present a novel information visualization technique for the graphical representation of causal relations, that is based on the metaphor of color pools spreading over time on a piece of paper. Messages between processes in the system a#ect the colors of their respective pool, making it possible to ..."
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Cited by 5 (2 self)
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We present a novel information visualization technique for the graphical representation of causal relations, that is based on the metaphor of color pools spreading over time on a piece of paper. Messages between processes in the system a#ect the colors of their respective pool, making it possible to quickly see the influences each process has received. This technique, called Growing Squares, has been evaluated in a comparative user study and shown to be significantly faster and more efficient for sparse data sets than the traditional Hasse diagram visualization. Growing Squares were also more e#cient for large data sets, but not significantly so. Test subjects clearly favored Growing Squares over old methods, naming the new technique easier, more efficient, and much more enjoyable to use.
Minimum depth graph embedding
 Proc. ESA’00, volume 1879 of LNCS
, 2000
"... Abstract. The depth of a planar embedding is a measure of the topological nesting of the biconnected components of the graph. Minimizing the depth of planar embeddings has important practical applications to graph drawing. We give a linear time algorithm for computing a minimum depth embedding of a ..."
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Cited by 3 (0 self)
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Abstract. The depth of a planar embedding is a measure of the topological nesting of the biconnected components of the graph. Minimizing the depth of planar embeddings has important practical applications to graph drawing. We give a linear time algorithm for computing a minimum depth embedding of a planar graphs whose biconnected components have a prescribed embedding. 1
Algorithm Engineering
 The Algorithmics Column (J. Diaz), Bulletin of the EATCS
, 2003
"... Algorithm Engineering is concerned with the design, analysis, implementation, tuning, debugging and experimental evaluation of computer programs for solving algorithmic problems. It provides methodologies and tools for developing and engineering e#cient algorithmic codes and aims at integrating a ..."
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Cited by 2 (1 self)
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Algorithm Engineering is concerned with the design, analysis, implementation, tuning, debugging and experimental evaluation of computer programs for solving algorithmic problems. It provides methodologies and tools for developing and engineering e#cient algorithmic codes and aims at integrating and reinforcing traditional theoretical approaches for the design and analysis of algorithms and data structures.
Drawing Database Schemas
 Software  Practice and Experience
"... A wide number of practical applications would benefit from automatically generated graphical representations of database schemas, in which tables are represented by boxes, and table attributes correspond to distinct stripes inside each table. Links, connecting attributes of two different tables, rep ..."
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Cited by 2 (0 self)
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A wide number of practical applications would benefit from automatically generated graphical representations of database schemas, in which tables are represented by boxes, and table attributes correspond to distinct stripes inside each table. Links, connecting attributes of two different tables, represent referential constraints or join relationships, and may attach arbitrarily to the left or to the right side of the stripes representing the attributes. To our knowledge no drawing technique is available to automatically produce diagrams in such strongly constrained drawing convention. In this paper we provide a polynomial time algorithm for solving this problem and test its efficiency and effectiveness against a large test suite. Also, we describe an implementation of a system that uses such an algorithm and we study the main methodological problems we faced in developing such a technology.
A New Parallel Algorithm for Planarity Testing
, 2003
"... This paper presents a new parallel algorithm for planarity testing based upon the work of Klein and Reif [14]. Our new approach gives correct answers on instances that provoke false positive and false negative results using Klein and Reif's algorithm. The new algorithm has the same complexity bounds ..."
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Cited by 1 (1 self)
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This paper presents a new parallel algorithm for planarity testing based upon the work of Klein and Reif [14]. Our new approach gives correct answers on instances that provoke false positive and false negative results using Klein and Reif's algorithm. The new algorithm has the same complexity bounds as Klein and Reif's algorithm and runs in O n processors of a Concurrent Read Exclusive Write (CREW) Parallel RAM (PRAM). Implementations of the major steps of this parallel algorithm exist for symmetric multiprocessors and exhibit speedup when compared to the best sequential approach. Thus, this new parallel algorithm for planarity testing lends itself to a highperformance sharedmemory implementation.
Incremental Convex Planarity Testing
, 2001
"... An important class of planar straightline drawings of graphs are convex drawings, in which all the faces are drawn as convex polygons. A planar graph is said to be convex planar if it admits a convex drawing. We give a new combinatorial characterization of convex planar graphs based on the decompos ..."
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An important class of planar straightline drawings of graphs are convex drawings, in which all the faces are drawn as convex polygons. A planar graph is said to be convex planar if it admits a convex drawing. We give a new combinatorial characterization of convex planar graphs based on the decomposition of a biconnected graph into its triconnected components. We then consider the problem of testing convex planarity in an incremental environment, where a biconnected planar graph is subject to online insertions of vertices and edges. We present a data structure for the online incremental convex planarity testing problem with the following performance, where n denotes the current number of vertices of the graph: (strictly) convex planarity testing takes O(1) worstcase time, insertion of vertices takes O(log n) worstcase time, insertion of edges takes O(log n) amortized time, and the space requirement of the data structure is O(n).