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Worcester Polytechnic Institute. XmdvTool Interactive Visual Data Exploration System for High-dimensional Data Sets. http://davis.wpi.edu/~xmdv. Matthew O. Ward, Elke A. Rundensteiner, Jing Yang, Punit Doshi, Geraldine Rosario, Allen R. Martin, Ying-Huey Fua, Daniel Stroe .

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Worcester polytechnic institute

Worcester Polytechnic Institute

XmdvTool

Interactive Visual Data Exploration System for High-dimensional Data Sets

http://davis.wpi.edu/~xmdv

Matthew O. Ward, Elke A. Rundensteiner,

Jing Yang, Punit Doshi, Geraldine Rosario,

Allen R. Martin, Ying-Huey Fua, Daniel Stroe

This work partially funded by NSF Grants IIS-9732897, IRIS-9729878 and IIS-0119276


Xmdvtool features

XmdvTool Features

  • Hierarchical visualization and interaction tools for exploring very large high-dimensional data sets to discover patterns, trends and outliers

  • Applications:

    • Bioterrorism Detection

    • Bioinformatics and Drug Discovery

    • Space Science

    • Geology and Geochemistry

    • Systems Monitoring and Performance Evaluation

    • Economics and Business

    • Simulation Design and Analysis

  • Multi-platform support (Unix, Linux, Windows)

  • Public domain software:http://davis.wpi.edu/~xmdv


Worcester polytechnic institute

Xmdv: Main Features

  • Scale-up to High Dimensions: Visual Hierarchical Dimension Reduction

  • Scale-up to Large Data Sets: Interactive Hierarchical Displays, Database Backend with Minmax Encoding, Semantic Caching and Adaptive Prefetching

  • Interlinked Multi-Displays: Parallel Coordinates, Glyphs, Scatterplot Matrices, Dimensional Stacking

  • Visual Interaction Tools:N-Dimensional Brushes, Structure-Based Brushing, InterRing


Scale up for large number of dimensions

Scale-Up for Large Number of Dimensions

Solution to High Dimensional Datasets:

  • Group Similar Dimensions into Dimension Hierarchy

  • Navigate Dimension Hierarchy by InterRing

  • Form Lower Dimensional Spaces by Dimension Clusters

  • Convey Dimension Cluster Information by Dissimilarity Display


Worcester polytechnic institute

Visual Hierarchical Dimension Reduction Process


Worcester polytechnic institute

Visual Hierarchical Dimension Reduction Process

A 42-dimensional Data Set

A 4-Dimensional Subspace

Dimension Hierarchy Interaction Tool: InterRing


Worcester polytechnic institute

InterRing - Dimension Hierarchy Navigation and Manipulation

Roll-up/Drill-down Rotate Zoom in/out

Modify

Distort


Dissimilarity display

Dissimilarity Display

Three Axes Method

Diagonal Plot Method

Axis Width Method

Mean-Band Method


Scale up for large number of records

Scale-up for Large Number of Records

Solution to Large Scale Datasets:

  • Group Similar Records into Data Hierarchy

  • Navigate Data Hierarchy by Structure-Based Brushing

  • Represent Data Clusters by Mean-Band Method

  • Provide Database Backend Support using MinMax Tree, Caching, Prefetching


Worcester polytechnic institute

Interactive Hierarchical Display

2D example

Hierarchical Clustering

Structure-Based Brushing


Worcester polytechnic institute

Interactive Hierarchical Display

Flat Display

Hierarchical Display

Mean-Band Method in Parallel Coordinates


Worcester polytechnic institute

Interactive Hierarchical Display

Flat Display

Hierarchical Display

Mean-Band Method in Parallel Coordinates


Scalability of data access

Scalability of Data Access

  • Approach

    • Attach database system to visualization front-end

  • MinMax hierarchy encoding

    • Key idea: avoid recursive processing

    • Pre-computed

  • Caching

    • Key idea: reduce response time and network traffic

  • Prefetching

    • Key idea: use application hints and predict user patterns

    • Performed during idle time


  • Scalability of data access minmax hierarchy encoding

    Pre-compute object positions

    level-of-detail (L)

    extent values (x,y)

    preserve tree structure

    New query semantics

    objects are now rectangles

    select objects that touch L

    select objects that touch (x, y)

    structure-based brush = intersection of two selections

    level of detail

    L

    x

    y

    extent values

    L

    query = (x, y, L)

    x

    y

    Scalability of Data Access:MinMax Hierarchy Encoding


    Worcester polytechnic institute

    Scalability of Data Access: Caching

    • Purpose

      • reduce response time and network traffic

  • Issues

    • visual query cannot directly translate into object IDs

    • high-level cache specification to avoid complete scans

  • Semantic caching

    • queries are cached rather than objects

    • minimize cost of cache lookup

    • dynamically adapt cached queries to patterns of queries


  • Worcester polytechnic institute

    Scalability of Data Access: Prefetching

    • Strategy

      • Speculative (no specific hints)

        • navigation remains local

        • both user and data set influence exploration

    • Adaptive (strategy changes over time)

      • Evolves as more knowledge becomes available

  • Non-pure (interruptible prefetching)

    • leave buffer in consistent state

  • Requirements

    • non-pure prefetching + large transactions & small object size + semantic caching  small granularity (object level)

    • speculative, non-pure prefetcher  cache replacement policy + guessing method


  • Worcester polytechnic institute

    Scalability of Data Access: Experimental Evaluation

    • Conclusions:

    • Caching reduces response time by 80%

    • Prefetching further reduces response time by 30%

    • Designing better prefetching strategies might help further reduce response time


    Worcester polytechnic institute

    m(n)

    (m-1)

    m

    (m+1)

    m(n+1)

    m(n-1)

    m(n-2)

    m(n)

    Hot Regions

    Current Navigation Window

    m(n-1)

    m(n+1)

    m(n-2)

    Scalability of Data Access: Prefetching

    Mean Strategy

    Random Strategy

    Direction Strategy

    Localized Speculative Strategies

    Exponential Weight Average Strategy

    Focus Strategy

    Data Set Driven Strategy

    Vector Strategies


    Worcester polytechnic institute

    OFF-LINE PROCESS

    MinMax

    Labeling

    Hierarchical

    Data

    DB

    DB

    DB

    Flat

    Data

    Loader

    Schema

    Info

    User

    Translator

    GUI

    Rewriter

    MEMORY

    Exploration

    Variables

    Buffer

    Queries

    Prefetcher

    Library:

    Buffer

    Estimator

    ON-LINE PROCESS

    Random

    Direction

    Focus

    Mean

    EWA

    Xmdv System Implementation

    • Tools

      • C/C++

      • TCL/TK

      • OpenGL

      • Oracle 8i

      • Pro*C


    Publications available at http davis wpi edu xmdv

    Publications (available at http://davis.wpi.edu/~xmdv)

    • Jing Yang, Matthew O. Ward and Elke A. Rundensteiner, "InterRing: An Interactive Tool for Visually Navigating and Manipulating Hierarchical Structures",InfoVis 2002, to appear

    • Punit R. Doshi, Elke A. Rundensteiner, Matthew O. Ward and Daniel Stroe, “Prefetching For Visual Data Exploration.”

      Technical Report #: WPI-CS-TR-02-07, 2002

    • Jing Yang, Matthew O. Ward and Elke A. Rundensteiner, “Interactive Hierarchical Displays: A General Framework for Visualization and Exploration of Large Multivariate Data Sets”, Computers and Graphics Journal, 2002, to appear

    • Daniel Stroe, Elke A. Rundensteiner and Matthew O. Ward, “Scalable Visual Hierarchy Exploration”, Database and Expert Systems Applications, pages 784-793, Sept. 2000

    • Ying-Huey Fua, Matthew O. Ward and Elke A. Rundensteiner, “Hierarchical Parallel Coordinates for Exploration of LargeDatasets”, IEEE Proc. of Visualization, pages 43-50, Oct. 1999

    • Ying-Huey Fua, Matthew O. Ward and Elke A. Rundensteiner, “Navigating Hierarchies with Structure-Based Brushes”, IEEE Proceedings of Visualization, pages 43-50, Oct. 1999


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