PPT-Visualization Knowledge Query Language (VKQL) Workshop

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. Zhonghua. . Qu. and . Ovidiu. . Daescu. December. . 24, 2009. University of Texas at Dallas. Introduction. Assisting Visualization System. Implementation. . Examples. Conclusion. Outline. Introduction. Murali. Mani, . Mohamad. . Alawa. , . Arunlal. . Kalyanasundaram. University of Michigan, Flint. Presented at IDEAS 2011.. Provenance Metadata. Data about origins of data. Applications:. Check whether data item is valid – in health records. Ray Hoff (UMBC). Amy Huff (Battelle Memorial Inst.). Shobha. . Kondragunta. (NESDIS). AQPG is in its third year. Two Workshops have followed this paradigm:. 3. rd. AQPG Workshop. Was to be held on November 1, 2012, but defeated by Sandy… now will be held March 14, 2013. Shengliang. . Dai. Background . Queries over large scale (petabyte) data bases often mean waiting overnight for a result to come back. . Scale costs time. . Potential. . avenues of exploration are ignored because the costs are perceived to be too high to run or even propose them. . Jianfeng Gao, MSR. (Joint work with . Jian. Huang, . Jiangbo. Miao, Xiaolong Li, Kuansan Wang and Fritz Behr). Outline. N-gram language model (LM) ABC. N-gram LM at Microsoft. Bing-It-On-. Ngram. Building Web scale N-gram LM. 2014 Summer Internship - Tetherless World Constellation. Sumithra Gnanasekar. Lakshmi Chenicheri. Objective. Visualize Minimum Information about a Marker Gene Sequence (. MiMarks. ) compliant datasets. Zi Yang. , Eric Nyberg. Language Technologies Institute. School of Computer Science. Carnegie Mellon University. {. ziy. , . ehn. }@. cs.cmu.edu. Outline. Background. Problem Definition. Proposed Approach. Creates interactive visualization . and dashboards . Fast and easy to use. Connect to any . data regardless . of its size and . complexity. To present . data from multiple sources. It gives . you . the ability . Starting up the Program. 2. Creating a Data Visualization . Using Tableau Public. Try 1. Creating a Data Visualization . Using Tableau Public. 3. Open Data. Creating a Data Visualization . Using Tableau Public. 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. Michael Johnson. 1. , Thomas Villani. 1, . Nick Crider. 1. ,Colleen Wojenski. 2 . 1. Visikol Inc., North Brunswick, NJ, 08902, 2. Product Safety Labs, Dayton, NJ, 08810. Abstract. Developmental and reproductive toxicology (DART) studies are required for new chemical entities going to market to ensure that there are not adverse developmental consequences associated with the chemical. These studies involve dosing a pregnant female animal (e.g. mouse, rat, rabbit) with the compound and evaluating the effect of the chemical on the offspring. This evaluation involves the parallel processes of histopathology and gross morphological evaluation wherein the most time-consuming component of DART studies is the performance of gross skeletal evaluation. Since the introduction of Thalidomide into the marketplace, close attention has been paid to the impact of chemicals on skeletal development and DART studies require the visualization of gross skeletal morphology for abnormalities. The current methodology for conducting skeletal evaluation involves digesting soft tissue using a strong base (potassium hydroxide) and staining the bone with alizarin and cartilage in some cases with . Course Goal. The goal of this course is to teach end users of ctcLink PeopleSoft to find and retrieve Queries and Reports in the most effective manner.. Course Learning Objectives. At the end of this course users will:. 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. .