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Information Visualization
Published in Alexandru Telea, Data Visualization, 2014
Figure 11.13 shows a first example of the hierarchical graph layout. The depicted graph illustrates the evolution of the UNIX operating system.6 The vertical axis roughly indicates the timeline, with the early versions of UNIX depicted at the top and the most-recent versions at the bottom, respectively. The edge directions indicate the same evolution pattern. The twelve horizontal layers roughly correspond to the number of evolution steps, or phases, that the original “5th Edition” UNIX has evolved through. This picture, generated from the directed graph description using the tool from the open-source GraphViz graph-drawing software [GraphViz 14], gives intuitive insight into how the 47 versions of the UNIX system are related to each other from an evolution perspective. From a single initial version, no less than seven versions have emerged after two evolution steps. We see, however, that the version proliferation stays contained, as only four versions (FreeBSD, NetBSD, OpenBSD, and System V.4) are present on the latest evolution layer. We also notice how splines are used to map edges that connect nodes on nonconsecutive layers.
Multilingual and Multimodal Access
Published in Sarika Jain, Understanding Semantics-Based Decision Support, 2021
Visualization of a multimedia ontology follows a series of steps. First, the MOWL parser is required to parse the domain ontology and deploy it into a Bayesian network. To carry out that process, we require APIs like NanoXML (a non-validating parser for Java) and Netica-J, which provides support to the Bayesian network. Netica-J is used to represent relationships between concepts even in the presence of uncertainty. Parsing of the disaster multimedia ontology results in a .dot file that contains its generated Bayesian network. This .dot file is fed into a tool like GVedit (from Graphviz) to decode it into an image.
Ontologies for Knowledge Representation
Published in Archana Patel, Narayan C. Debnath, Bharat Bhushan, Semantic Web Technologies, 2023
This tool helps to visualize Protégé ontologies using Graphviz which is a highly sophisticated graph visualization software developed by AT&T. The tool is highly configurable that allows selecting a particular set of classes or instances to visualize only a certain part of the ontology. The slots and slot edges can also be displayed and different colors can be specified to nodes and edges. Different closure operators can be used to visualize the vicinity of classes and sub-classes [37].
Automated simulation and verification of process models discovered by process mining
Published in Automatika, 2020
Ivona Zakarija, Frano Škopljanac-Mačina, Bruno Blašković
The process models we discovered from the preprocessed log data using our process discovery method are finite state automatons, encoded as directed graphs with labelled nodes and edges in Graphviz2 dot textual format. Therefore, they are static files that can be exported to standard image or document formats, e.g. JPEG, PNG, SVG or PDF. As an example, Figure 7 shows the initial imperfect process model and a more complex process model, both discovered by our process discovery method from the same input log data. Process mining experts can use these images to check and validate process models manually. Nevertheless, even for simpler process models (e.g. Figure 7a) we should not always rely only on manual and visual inspection methods.
The research of endless loop detection method based on the basic path
Published in International Journal of Computers and Applications, 2020
Xuexin Gao, Yongmin Mu, Meie Shen
PyGraphviz is used in this paper to generate control flow graphs. PyGraphviz is the interface provided by the Graphviz view layout and visual packages for Python. It can be used to create, edit, read, write, and draw by changing the Graphviz graph data structure. The connection relationship between nodes is read by traversing and parsing the JSON file in this paper. Edges are added to the diagram by using function add_edge and the control flow graph is drawn by using function draw. The specific algorithm is described as follows.
Evaluating graphical manipulations in automatically laid out LineSets
Published in Behaviour & Information Technology, 2021
Dominique Tranquille, Gem Stapleton, Jim Burton, Peter Rodgers
Initially, we created 24 Default LineSets from the 24 simplified SNAP datasets using the LineSets software (Alper et al. 2011). We chose to use the default settings as these have been provided by the software designers, informed by usability studies. This software overlays lines on an already drawn graph and, so, the networks had to be drawn first. Edge lists, derived from real data following the process described in Section 3.2, were imported into GraphViz (Gansner and North 2000) in order to create a node graph in SVG format along with a text file containing coordinates of each node in each diagram. The coordinates of each node were then exported to an XML file format which could be read by the automated LineSet layout software (Alper et al. 2011) specifying where to plot the points along each set-line. Set memberships for each node were specified in the XML file so that the set-lines could be generated. Where a node belonged to two or more sets, an instance of the node had to be created for each set to which it belonged so that the lines crossed at that point. As the LineSet software does not facilitate file exports, screenshots of the generated set-lines were imported into Inkscape (Gould, Harrington, and Hurst 2003). This allowed the set-lines to be manually traced over using the Bezier curves tool, which was a necessary step in order to generate the Treated LineSets and high-resolution images. The set-lines, with 4 pixels thickness, were then overlaid on top of the graph generated in step 1; we emphasise that this process preserved the paths followed by the set-lines as produced by the LineSets software. In each diagram, the LineSets software allocated each set-line a colour, which was also preserved when we re-drew the set-lines. The colour palletes used by the two types of Default diagrams can be seen in Figures 14 and 15. In all cases, the diagrams were drawn in an area no larger than pixels.