PPT-Scalable Data Visualization and Social Data Analysis

Jeffrey Heer Stanford University Joe Hellerstein UC Berkeley TexPoint fonts used in EMF Read the TexPoint manual before you delete this box A A A Visualization Reference Model. Zhonghua. . Qu. and . Ovidiu. . Daescu. December. . 24, 2009. University of Texas at Dallas. Introduction. Assisting Visualization System. Implementation. . Examples. Conclusion. Outline. Introduction. Nicholas Del . Rio. University of Texas at El Paso Computer Science. Workshop Objectives. U. nderstand . notion . and purpose of visualization . queries. Understand why writing queries may be easier that writing visualization programs. 2014 Summer Internship - Tetherless World Constellation. Sumithra Gnanasekar. Lakshmi Chenicheri. Objective. Visualize Minimum Information about a Marker Gene Sequence (. MiMarks. ) compliant datasets. Yingcai Xiao. Computation with and without Visual Assistance. 67 x 89 = ? . Visualized Data Analysis. 67. x 89. ---------. 603. + 536. ---------. 5963. . GLIF 2015, Prague. Andrew Lee. leea@indiana.edu. 1. Open. , privacy-aware, network measurement, analysis, and visualization service . Designed to address the needs of today’s international networks. David Granholm. , Senior Director PM . Matt Milella, VP Engineering. Oracle Analytics. October 4, 2017. 2. 3. Oracle Analytic Apps Portfolio. OTBI. DV. Content. Packs. Oracle Analytic Cloud (OAC). Next-Gen. iPhone. Capstone Team#5. Fall 2009. Table of Contents. Topic. Slide Number. Team information. ………………………………………………3. Instructor/Mentors Information. ………………………………………………4. ParaViewWeb. Web3D – . Paris 2011. Julien. . Jomier. , . Kitware. . julien.jomier@kitware.com. Motivation. Acquiring Data (2). Tissue rotates. Knife advances. This tissue ribbon is collected by a submerged conveyor belt. Spring 2018. Course Introduction. What the course is about. What will you learn. Teaching team. Schedule. What you need to do to succeed. 1. Course overview. Visualization analysis & design. What visualization can do?. COS 418: Distributed Systems. Lecture . 14. Wyatt Lloyd. Consistency Hierarchy. Linearizability. Sequential Consistency. Causal+ Consistency. Eventual Consistency. e.g., RAFT. e.g., Bayou. e.g., Dynamo. Chong Ho (Alex) Yu. Agenda. What is data visualization?. What isn’t data visualization?. What is the difference between dynamic, semi-dynamic, and non-dynamic (static) visualization systems?. Why do we need data visualization?. Cherdyntsev E.S.. We have now covered the start and the end of the visualization . pipeline, . namely getting data into the computer, and, on the . human side. , how perception and cognition help us interpret images. . Jamal . Alsakran. . Kent . State University, . Ohio. Ye Zhao . Kent . State University, Ohio . . Yang . Chen . University . of North Carolina -. Large scale computing systems. Scalability . issues. Low level and high level communication abstractions in scalable systems. Network interface . Common techniques for high performance communication.