Rendering UML Class Diagrams to Support Layout Design
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The Hitchhiker's Guide to Graph Exchange Formats
The Hitchhiker’s Guide to Graph Exchange Formats Prof. Matthew Roughan [email protected] http://www.maths.adelaide.edu.au/matthew.roughan/ Work with Jono Tuke UoA June 4, 2015 M.Roughan (UoA) Hitch Hikers Guide June 4, 2015 1 / 31 Graphs Graph: G(N; E) I N = set of nodes (vertices) I E = set of edges (links) Often we have additional information, e.g., I link distance I node type I graph name M.Roughan (UoA) Hitch Hikers Guide June 4, 2015 2 / 31 Why? To represent data where “connections” are 1st class objects in their own right I storing the data in the right format improves access, processing, ... I it’s natural, elegant, efficient, ... Many, many datasets M.Roughan (UoA) Hitch Hikers Guide June 4, 2015 3 / 31 ISPs: Internode: layer 3 http: //www.internode.on.net/pdf/network/internode-domestic-ip-network.pdf M.Roughan (UoA) Hitch Hikers Guide June 4, 2015 4 / 31 ISPs: Level 3 (NA) http://www.fiberco.org/images/Level3-Metro-Fiber-Map4.jpg M.Roughan (UoA) Hitch Hikers Guide June 4, 2015 5 / 31 Telegraph submarine cables http://en.wikipedia.org/wiki/File:1901_Eastern_Telegraph_cables.png M.Roughan (UoA) Hitch Hikers Guide June 4, 2015 6 / 31 Electricity grid M.Roughan (UoA) Hitch Hikers Guide June 4, 2015 7 / 31 Bus network (Adelaide CBD) M.Roughan (UoA) Hitch Hikers Guide June 4, 2015 8 / 31 French Rail http://www.alleuroperail.com/europe-map-railways.htm M.Roughan (UoA) Hitch Hikers Guide June 4, 2015 9 / 31 Protocol relationships M.Roughan (UoA) Hitch Hikers Guide June 4, 2015 10 / 31 Food web M.Roughan (UoA) Hitch Hikers -
A DSL for Defining Instance Templates for the ASMIG System
A DSL for defining instance templates for the ASMIG system Andrii Kovalov Dissertation 2014 Erasmus Mundus MSc in Dependable Software Systems Department of Computer Science National University of Ireland, Maynooth Co. Kildare, Ireland A dissertation submitted in partial fulfilment of the requirements for the Erasmus Mundus MSc Dependable Software Systems Head of Department : Dr Adam Winstanley Supervisor : Dr James Power Date: June 2014 Abstract The area of our work is test data generation via automatic instantiation of software models. Model instantiation or model finding is a process of find- ing instances of software models. For example, if a model is represented as a UML class diagram, the instances of this model are UML object diagrams. Model instantiation has several applications: finding solutions to problems expressed as models, model testing and test data generation. There are sys- tems that automatically generate model instances, one of them is ASMIG (A Small Metamodel Instance Generator). This system is focused on a `problem solving' use case. The motivation of our work is to adapt ASMIG system for use as a test data generator and make the instance generation process more transparent for the user. In order to achieve this we provided a way for the user to interact with ASMIG internal data structure, the instance template graph via a specially designed graph definition domain-specific language. As a result, the user is able to configure the instance template in order to get plausible instances, which can be then used as test data. Although model finding is only suitable for obtaining test inputs, but not the expected test outputs, it can be applied effectively for smoke testing of systems that process complex hierarchic data structures such as programming language parsers. -
GOLD 3 Un Lenguaje De Programación Imperativo Para La Manipulación De Grafos Y Otras Estructuras De Datos
GOLD 3 Un lenguaje de programación imperativo para la manipulación de grafos y otras estructuras de datos Autor Alejandro Sotelo Arévalo UNIVERSIDAD DE LOS ANDES Asesora Silvia Takahashi Rodríguez, Ph.D. UNIVERSIDAD DE LOS ANDES TESIS DE MAESTRÍA EN INGENIERÍA DEPARTAMENTO DE INGENIERÍA DE SISTEMAS Y COMPUTACIÓN FACULTAD DE INGENIERÍA UNIVERSIDAD DE LOS ANDES BOGOTÁ D.C., COLOMBIA ENERO 27 DE 2012 > > > A las personas que más quiero en este mundo: mi madre Gladys y mi novia Alexandra. > > > Abstract Para disminuir el esfuerzo en la programación de algoritmos sobre grafos y otras estructuras de datos avanzadas es necesario contar con un lenguaje de propósito específico que se preocupe por mejorar la legibilidad de los programas y por acelerar el proceso de desarrollo. Este lenguaje debe mezclar las virtudes del pseudocódigo con las de un lenguaje de alto nivel como Java o C++ para que pueda ser fácilmente entendido por un matemático, por un científico o por un ingeniero. Además, el lenguaje debe ser fácilmente interpretado por las máquinas y debe poder competir con la expresividad de los lenguajes de propósito general. GOLD (Graph Oriented Language Domain) satisface este objetivo, siendo un lenguaje de propósito específico imperativo lo bastante cercano al lenguaje utilizado en el texto Introduction to Algorithms de Thomas Cormen et al. [1] como para ser considerado una especie de pseudocódigo y lo bastante cercano al lenguaje Java como para poder utilizar la potencia de su librería estándar y del entorno de programación Eclipse. Índice general I Preliminares 1 1 Introducción 2 1.1 Resumen ............................................... 2 1.2 Contexto............................................... -
Preserving Command Line Workflow for a Package Management System
To appear in IEEE Transactions on Visualization and Computer Graphics Preserving Command Line Workflow for a Package Management System using ASCII DAG Visualization Katherine E. Isaacs and Todd Gamblin Fig. 1: ASCII depiction of the package dependency graph of dia (40 nodes), as specified in the Spack package management tool. The dependency cairo and its direct neighbors have been interactively highlighted. Abstract— Package managers provide ease of access to applications by removing the time-consuming and sometimes completely prohibitive barrier of successfully building, installing, and maintaining the software for a system. A package dependency contains dependencies between all packages required to build and run the target software. Package management system developers, package maintainers, and users may consult the dependency graph when a simple listing is insufficient for their analyses. However, users working in a remote command line environment must disrupt their workflow to visualize dependency graphs in graphical programs, possibly needing to move files between devices or incur forwarding lag. Such is the case for users of Spack, an open source package management system originally developed to ease the complex builds required by supercomputing environments. To preserve the command line workflow of Spack, we develop an interactive ASCII visualization for its dependency graphs. Through interviews with Spack maintainers, we identify user goals and corresponding visual tasks for dependency graphs. We evaluate the use of our visualization through a command line-centered study, comparing it to the system’s two existing approaches. We observe that despite the limitations of the ASCII representation, our visualization is preferred by participants when approached from a command line interface workflow. -
Survey of Graph Tools Used by Github Projects
Supplemental Material for “Preserving Command Line Workflow for a Package Management System using ASCII DAG Visualization” Katherine E. Isaacs and Todd Gamblin Fig. 1: Dependency Graph Characteristics for Tool Blocks Tool # Nodes # Edges Layers Nodes per Layer Question Question Layers GraphViz 11 22 4 1-2-5-3 paths 1 & 3 git-like 11 22 4 1-2-5-3 paths 1 & 4 graphterm 11 22 5 1-1-2-4-3 paths 1 & 4 GraphViz 17 27 6 1-1-4-2-6-3 dependencies 5 git-like 17 26 7 1-2-4-2-1-4-3 dependencies 4 graphterm 18 26 5 1-4-6-5-2 dependencies 4 GraphViz 22 45 7 1-1-1-4-6-6-3 paths 1 & 5 git-like 22 45 7 1-1-1-4-6-6-3 paths 1 & 5 graphterm 22 45 7 1-1-1-4-6-6-3 paths 1 & 5 GraphViz 34 62 7 1-2-4-5-5-11-5-1 dependencies 8 paths 1 & 7 git-like 33 60 7 1-1-4-5-5-11-5-1 dependencies 6 paths 1 & 7 graphterm 34 61 7 1-2-4-5-5-11-5-1 dependencies 6 paths 1 & 7 1 EXPERIMENTAL OBJECTS USED IN STUDY We provide more details about the experimental objects used in the We included machine learning network visualizations but not sentence study. Fig. 1 appends information about the number of layers per structure dependencies in natural language processing or scene graph graph, the division of nodes in each layer, and the layers on which overlays in computer vision. -
Lesson 10: Graph Visualisation and Analysis
IS428 Visual Analytics for Business Intelligence 10/29/2015 Lesson 10: Graph Visualisation and Analysis Lesson 10 Graph Visualisation and Analysis: Methods and Applications Mentor: Dr. Kam Tin Seong Associate Professor of Information Systems (Practice) School of Information Systems, Singapore Management University Content • Motivation of graph visualisation • Graph Visualisation in Actions • Typology of network data • Graph data file formats • Graph Layout Techniques • Network visualisation and analysis process model • Network Metrics • Network visualisation and analysis tools – Gephi – NodeXL 10-1 IS428 Visual Analytics for Business Intelligence 10/29/2015 Lesson 10: Graph Visualisation and Analysis Network in real World • Physical – Transportation (i.e. road, port, rail, etc) – Utility (electricity, water, gas, network cable, etc) – Natural (river, etc) • Abstract – Social media (i.e. e-mail, Facebook, Twitter, Wikipedia, etc) – Organisation (i.e. NGO, politics, customer-company, staff-to-staff, criminal, terrorist, disease, etc) 3 Classical Graph Theory • The Seven Bridges of Königsberg is a historically notable problem in mathematics. Its negative resolution by Leonhard Euler in 1735 laid the foundations of graph theory and prefigured the idea of topology. Source: http://en.wikipedia.org/wiki/Seven_Bridges_of_K%C3%B6nigsberg 4 10-2 IS428 Visual Analytics for Business Intelligence 10/29/2015 Lesson 10: Graph Visualisation and Analysis Classical Graph Visualisation and Analysis • Using sociogram, also know as network graph Early 20th-century -
Unravelling Graph Exchange File Formats
1 Unravelling Graph-Exchange File Formats Matthew Roughan and Jonathan Tuke Abstract—A graph is used to represent data in which the this context is not purely about syntax. Exchange also requires relationships between the objects in the data are at least as common definitions of the meaning of the attributes. important as the objects themselves. Over the last two decades nearly a hundred file formats have been proposed or used On the other hand, file size is not a primary consideration. to provide portable access to such data. This paper seeks to Hence many exchange formats pay little attention to this and review these formats, and provide some insight to both reduce related details (e.g., read/write performance). the ongoing creation of unnecessary formats, and guide the We concentrate on exchange formats, but some of the development of new formats where needed. formats considered here were not originally developed with exchange in mind, but have become de facto exchange formats I. INTRODUCTION through use. In these cases we see reversals of objectives XCHANGE of data is a basic requirement of scientific compared to some purpose-built exchange formats. We shall E research. Accurate exchange requires portable file for- therefore consider a large range of such features for compari- mats, where portability means the ability to transfer (without son, noting as we do so that as exchange of very large datasets extraordinary efforts) the data both between computers (hard- becomes important, the requirements will change. ware and operating system), and between software (different Many of the formats presented may seem obsolete. -
YARS-PG: Serialization for Property Graphs
YARS-PG: Serialization for Property Graphs Dominik Tomaszuk1 Renzo Angles2,3 Łukasz Szeremeta1 Karol Litman1 Diego Cisterna3 1Institute of Informatics, 2Department of Computer Science, 3Center for Semantic Web Research, University of Bialystok, Bialystok, Poland Universidad de Talca, Curicó, Chile Santiago, Chile Abstract Our proposal Graph serialization is critical for the evolution of graph-oriented Understanding the use cases and requirements of different people systems and applications. Unfortunately, there is still no uni- will help define the needs and the priority of features. Finally, versal serialization for property graphs that will cover all main on fundamentals of this, we propose YARS-PG [5] that is platform features of graph databases systems. We propose YARS-PG prop- independent and extensible serialization. Our serialization supports erty graphs serialization which is simple, extensible, and platform all the features allowed by the current database systems based on independent. YARS-PG supports all the features allowed by the the property graph data model, and can be adapted to work with current database systems based on the property graph data model, various visualization software, database-driven systems and other and can be adapted in the future to work with various database graph-oriented tools. systems, visualization software and other graph-oriented tools. 1 <"Author01">{"Author"}["fname":"John","lname":"Smith"] 2 <"Author02">{"Author"}["fname":"Alice","lname":"Brown"] 3 <"EI01">{"Entry","InProc"}["title":"Serialization for...", Introduction "numpages":10,"keyword":"Graph database"] Data serialization is fundamental in data management as it al- 4 <"EA01">{"Entry","Article"}["title":"Property Graph...", lows to support database exchange, systems benchmarking, data "numpages":10,"keyword":["Query", "Graph"]] visualization and presentation. -
Lesson 10 Visualising and Analysing Network Data
ISSS608 Visual Analytics and Applications 11/18/2015 Lesson 10: Visualising and Analysing Network Data Lesson 10 Visualising and Analysing Network Data Mentor: Dr. Kam Tin Seong Associate Professor of Information Systems (Practice) School of Information Systems, Singapore Management University Content • Introduction to Graph Visual Analytics • Graph Visualisation in Actions • Basic Principles of Graph – Network data sets – Graph data format • Network Visualisation and Analysis – Network visualisation and analysis process model – Graph layouts and visual attributes – Network metrics • Network visualisation and analysis tools – NodeXL – Gephi 2 10-1 ISSS608 Visual Analytics and Applications 11/18/2015 Lesson 10: Visualising and Analysing Network Data Network in real World • Physical – Transportation (i.e. road, port, rail, etc) – Utility (electricity, water, gas, network cable, etc) – Natural (river, etc) • Abstract – Social media (i.e. e-mail, Facebook, Twitter, Wikipedia, etc) – Organisation (i.e. NGO, politics, customer-company, staff-to-staff, criminal, terrorist, disease, etc) 3 Classical Graph Theory • The Seven Bridges of Königsberg is a historically notable problem in mathematics. Its negative resolution by Leonhard Euler in 1735 laid the foundations of graph theory and prefigured the idea of topology. Source: http://en.wikipedia.org/wiki/Seven_Bridges_of_K%C3%B6nigsberg 4 10-2 ISSS608 Visual Analytics and Applications 11/18/2015 Lesson 10: Visualising and Analysing Network Data Classical Graph Visualisation and Analysis • Using -
Veritas Information Map User Guide
Veritas Information Map User Guide November 2017 Veritas Information Map User Guide Last updated: 2017-11-21 Legal Notice Copyright © 2017 Veritas Technologies LLC. All rights reserved. Veritas and the Veritas Logo are trademarks or registered trademarks of Veritas Technologies LLC or its affiliates in the U.S. and other countries. Other names may be trademarks of their respective owners. This product may contain third party software for which Veritas is required to provide attribution to the third party (“Third Party Programs”). Some of the Third Party Programs are available under open source or free software licenses. The License Agreement accompanying the Software does not alter any rights or obligations you may have under those open source or free software licenses. Refer to the third party legal notices document accompanying this Veritas product or available at: https://www.veritas.com/about/legal/license-agreements The product described in this document is distributed under licenses restricting its use, copying, distribution, and decompilation/reverse engineering. No part of this document may be reproduced in any form by any means without prior written authorization of Veritas Technologies LLC and its licensors, if any. THE DOCUMENTATION IS PROVIDED "AS IS" AND ALL EXPRESS OR IMPLIED CONDITIONS, REPRESENTATIONS AND WARRANTIES, INCLUDING ANY IMPLIED WARRANTY OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE OR NON-INFRINGEMENT, ARE DISCLAIMED, EXCEPT TO THE EXTENT THAT SUCH DISCLAIMERS ARE HELD TO BE LEGALLY INVALID. VERITAS TECHNOLOGIES LLC SHALL NOT BE LIABLE FOR INCIDENTAL OR CONSEQUENTIAL DAMAGES IN CONNECTION WITH THE FURNISHING, PERFORMANCE, OR USE OF THIS DOCUMENTATION. THE INFORMATION CONTAINED IN THIS DOCUMENTATION IS SUBJECT TO CHANGE WITHOUT NOTICE. -
Visualization of Gene Ontology and Cluster Analysis Results
Degree project Visualization of Gene Ontology and Cluster Analysis Results Author: Vladyslav Aleksakhin Date: 2012-08-20 Subject: Computer Science Level: Master Course code: 5DV00E Abstract The purpose of the thesis is to develop a new visualization method for Gene On- tologies and hierarchical clustering. These are both important tools in biology and medicine to study high-throughput data such as transcriptomics and metabolomics data. Enrichment of ontology terms in the data is used to identify statistically over- represented ontology terms, that give insight into relevant biological processes or functional modules. Hierarchical clustering is a standard method to analyze and visualize data to find relatively homogeneous clusters of experimental data points. Both methods support the analysis of the same data set, but are usually considered independently. However, often a combined view such as: visualizing a large data set in the context of an ontology under consideration of a clustering of the data. The result of the current work is a user-friendly program that combines two different views for analysing Gene Ontology and Cluster simultaneously. To make explorations of such a big data possible we developed new visualization approach. Keywords Graph Visualization, Gene Ontology, Hierarchical Clustering, Mappings, Interaction Technique, GML, JUNG. ii Acknowledgments I would like to express my gratitude to my supervisor Dr. Andreas Kerren and to my co-supervisor Ilir Jusufi. They encouraged and stimulated me in each step of this work. Moreover, my greatest gratitude goes to my father Oleksandr and my family that made my study possible, for their constant moral support even though thousands of kilometers separated us.