Magic Lenses for Interactive Database Visualization Ken Fishkin SoftBook Press, Inc.

Slides:



Advertisements
Similar presentations
What is jQuery Mobile? How to use it? Doncho Minkov Telerik Corporation Technical Trainer.
Advertisements

Microsoft Dynamics® SL
ORGANIZING THE CONTENT Physical Structure
® Page 1 Intel Compiler Lab – Intel Array Visualizer HDF Workshop VI December 5, 2002 John Readey
Magic Lenses for Interactive Database Visualization Ken Fishkin SoftBook Press, Inc.
Dynamic Queries for Visual Information Seeking Ben Shneiderman Jin Tong Hyunmo Kang Cmsc838 Sep. 28, 1999.
Lucene Part3‏. Lucene High Level Infrastructure When you look at building your search solution, you often find that the process is split into two main.
An Associative Broadcast Based Coordination Model for Distributed Processes James C. Browne Kevin Kane Hongxia Tian Department of Computer Sciences The.
SIMS 213: User Interface Design & Development Marti Hearst Thurs, March 6, 2003.
Automating Tasks With Macros
1 Query Languages. 2 Boolean Queries Keywords combined with Boolean operators: –OR: (e 1 OR e 2 ) –AND: (e 1 AND e 2 ) –BUT: (e 1 BUT e 2 ) Satisfy e.
1 Introducing Collaboration to Single User Applications A Survey and Analysis of Recent Work by Brian Cornell For Collaborative Systems Fall 2006.
Visualizating the Non-Visual: Spatial Analysis and Interaction with Information from Text Documents J.A. Wise, J.J. Thomas, K. Pennock, D. Lantrip, M.
1 Model View Controller. 2 Outline Review Definitions of MVC Why do we need it? Administiriva Changing the display Event flow Dragging at interactive.
SIMS 213: User Interface Design & Development Marti Hearst Tues, March 5, 2002.
Infovis and data george, laura, tjerk.
Interfaces for Querying Collections. Information Retrieval Activities Selecting a collection –Lists, overviews, wizards, automatic selection Submitting.
February 12, 2009 Center for Hybrid and Embedded Software Systems Model Transformation Using ERG Controller Thomas H. Feng.
SWIM 1/9/20031 QoS in Data Stream Systems Rajeev Motwani Stanford University.
SIMS 213: User Interface Design & Development Marti Hearst Thurs Feb 22, 2001.
Visualization By: Simon Luangsisombath. Canonical Visualization  Architectural modeling notations are ways to organize information  Canonical notation.
TIBCO Designer TIBCO BusinessWorks is a scalable, extensible, and easy to use integration platform that allows you to develop, deploy, and run integration.
Predicates and Quantifiers
Document (Text) Visualization Mao Lin Huang. Paper Outline Introduction Visualizing text Visualization transformations: from text to pictures Examples.
WEB DESIGN AND PROGRAMMING Introduction to Javascript.
NEST for Knowledge Assisted Analysis Petr Berka UEP, Praha Thanos Athanasiadis NTUA, Athens.
JASS 2005 Next-Generation User-Centered Information Management Information visualization Alexander S. Babaev Faculty of Applied Mathematics.
© 2006 Lawrenceville Press Slide 1 Chapter 3 Visual Basic Interface.
CSC 480 Software Engineering Lecture 19 Nov 11, 2002.
1-1 OBJ Copyright 2003, Paradigm Publishing Inc. Dr. Joseph Otto Silvia Castaneda Christopher deCastro CSULA Macromedia Flash MX Introduction.
Automating Database Processing Chapter 6. Chapter Introduction Design and implement user-friendly menu – Called navigation form Macros – Automate repetitive.
Combinational Logic 1.
Methodology - Conceptual Database Design. 2 Design Methodology u Structured approach that uses procedures, techniques, tools, and documentation aids to.
Testing Testing Techniques to Design Tests. Testing:Example Problem: Find a mode and its frequency given an ordered list (array) of with one or more integer.
Methodology - Conceptual Database Design
Copyright © Curt Hill Mathematical Logic An Introduction.
MS Access: Introduction 1Database Design. MS Access: Overview MS Access A Database Management System (DBMS) designed to create applications that organize,
Performing Calculations—1 of 2 In addition to using queries to retrieve, update, sort, and filter data in a database, you can use a query to perform calculations.
Introducing Python CS 4320, SPRING Lexical Structure Two aspects of Python syntax may be challenging to Java programmers Indenting ◦Indenting is.
Toolglasses and Magic Lenses. 2 Reading: Eric A. Bier, Maureen C. Stone, Ken Pier, William Buxton and Tony D. DeRose, “Toolglass and magic lenses: the.
Innovative UI Ideas Marti Hearst SIMS 213, UI Design & Development April 20, 1999.
Application of dependency graph to security protocol analysis Ilja Tšahhirov (joint work with Peeter Laud) Theory Days at Jõulumäe 5 Oct 2008.
C. Ahlberg & B. Shneiderman (1994)
Graphical Enablement In this presentation… –What is graphical enablement? –Introduction to newlook dialogs and tools used to graphical enable System i.
Database Architecture Course Orientation & Context.
Daniel A. Keim, Hans-Peter Kriegel Institute for Computer Science, University of Munich 3/23/ VisDB: Database exploration using Multidimensional.
BOĞAZİÇİ UNIVERSITY DEPARTMENT OF MANAGEMENT INFORMATION SYSTEMS MATLAB AS A DATA MINING ENVIRONMENT.
Thinking Mathematically
Dynamic Queries cs5984: Information Visualization Chris North.
SOEN 343 Software Design Section H Fall 2006 Dr Greg Butler
32nd International Conference on Very Large Data Bases September , 2006 Seoul, Korea Efficient Detection of Empty Result Queries Gang Luo IBM T.J.
Fundamentals, Design, and Implementation, 9/e Appendix B The Semantic Object Model.
Why IR test collections are so bad Mark Sanderson University of Sheffield.
An Extension of Table Lens CPSC 533 Information Visualization Course Project, Term 2, 2003 Fengdong Du.
Plug-In Architecture Pattern. Problem The functionality of a system needs to be extended after the software is shipped The set of possible post-shipment.
Lecture 11 Introduction to R and Accessing USGS Data from Web Services Jeffery S. Horsburgh Hydroinformatics Fall 2013 This work was funded by National.
EE 200 Design Tools Laboratory 14
Software Design and Architecture
Interactive Presentation Title
Implementing Language Extensions with Model Transformations
Interactive Presentation Title
Enhanced Dynamic Queries via Movable Filters
Dynamic Queries for Visual Information Seeking Ben Shneiderman
Expressions.
Introduction to Functional Programming in Racket
DEV354 Visual Studio 2005: New Enhancements For Debugging Applications
Automate Repetitive Programming Tasks: Effective SAS® Code Generators
Implementing Language Extensions with Model Transformations
Microsoft Access Tips and Tricks
Web Programming and Design
Presentation transcript:

Magic Lenses for Interactive Database Visualization Ken Fishkin SoftBook Press, Inc.

Magic Lenses - Ken Fishkin - Nov Traditional Database Queries n Use a Special Language  select title from movies where lead_actor=‘Connery, Sean’ and (year 1975) n Batch, non-visual

Magic Lenses - Ken Fishkin - Nov Dynamic Queries (example 1) n One selector per attribute

Magic Lenses - Ken Fishkin - Nov Dynamic Queries (example 2) n Selectors filter the display

Magic Lenses - Ken Fishkin - Nov Dynamic Queries (limitations) n designed for a small number of attributes n only global filters n can’t screen on an attribute more than once n no disjunctions n limited query set

Magic Lenses - Ken Fishkin - Nov Hybrid Techniques n language for ‘leaves’ of the query, n visual interface for compound queries n Still not all queries supported ContentDateContentContains is before contains Document Management 05/01/94 Visual Recall OS AndOr

Magic Lenses - Ken Fishkin - Nov Magic Lenses n Movable local filters, which transform the data underneath them in some way, be it visual (magnifying lens), semantic (misspelled words), or other

Magic Lenses - Ken Fishkin - Nov Merging Lenses into Queries n Put one attribute selector on a lens.

Magic Lenses - Ken Fishkin - Nov #1 - local filters

Magic Lenses - Ken Fishkin - Nov #2 - repeated attributes

Magic Lenses - Ken Fishkin - Nov #3 - arbitrary number of attrs. n Just stack ‘em up.

Magic Lenses - Ken Fishkin - Nov Consistent UI

Magic Lenses - Ken Fishkin - Nov Query Power n 2.5D order of windows implies a composition/evaluation order n Put an AND/OR toggle on the lens to indicate how it should compose: u A AND B --> above u A OR B --> above

Magic Lenses - Ken Fishkin - Nov And/or in action

Magic Lenses - Ken Fishkin - Nov Query Power(2) n NOT gets its own lens u A AND NOT (B OR C) F

Magic Lenses - Ken Fishkin - Nov Grouping n Introduce compound (grouped) lenses n Allows parenthesizing n allows macros n Conjunction + Negation + Grouping ==> support for arbitrary Boolean queries

Magic Lenses - Ken Fishkin - Nov Extensions n No need to have just ‘AND’ and ‘OR’ - could have any/all of the 16 possible combinations. n Could just have a ‘NAND’ mode, but that would be non-intuitive. And/Or/Not are most common.

Magic Lenses - Ken Fishkin - Nov Fuzzy Selectors n Selectors need not be ‘pass/fail’. False True False True

Magic Lenses - Ken Fishkin - Nov Selectors over [0..1]

Magic Lenses - Ken Fishkin - Nov Numerical Operators

Magic Lenses - Ken Fishkin - Nov Fuzzy Composition n Selectors on [0..1] implies composition on [0..1] n Replace AND by MIN, OR by MAX, NOT by complement n Presently, have implemented arithmetic (“DIFF”), statistical (“SQRT”), and fuzzy (“VERY”) n Many others possible

Magic Lenses - Ken Fishkin - Nov Fuzzy example

Magic Lenses - Ken Fishkin - Nov Missing Data - display

Magic Lenses - Ken Fishkin - Nov Missing Data - example

Magic Lenses - Ken Fishkin - Nov Missing Data - composition n How do composition operators handle it? We treat it like IEEE NaN

Magic Lenses - Ken Fishkin - Nov Conclusion (1995) n by merging Dynamic Queries with Magic Lenses, we keep the interactive, visual nature of queries, but add more functionality. n Future work: a slicker UI, user studies.

Magic Lenses - Ken Fishkin - Nov Conclusion (2000) n If this is so great, why doesn’t everyone use it? u Inter-app. Requires lots of “plumbing”, Xerox licensing. OS X? u Intra-app. Requires Xerox licensing. So far SGI only one determined enough to do it.