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VisDB : Database exploration using Multidimensional Visualization. Daniel A. Keim, Hans-Peter Kriegel Institute for Computer Science, University of Munich. Created By. Rohan Ladkhedkar Ajinkya Raulkar Vrushali Date Anuja Surgude. Contents. Introduction to VisDB Basic Idea of VisDB

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Visdb database exploration using multidimensional visualization

VisDB: Database exploration using Multidimensional Visualization

Daniel A. Keim, Hans-Peter Kriegel

Institute for Computer Science, University of Munich

Created by

Created By

  • RohanLadkhedkar

  • AjinkyaRaulkar

  • Vrushali Date

  • AnujaSurgude



  • Introduction to VisDB

  • Basic Idea of VisDB

  • Techniques used

    • Basic Visualization

    • Mapping 2D to Axis

    • Grouping the Dimensions

  • Working

  • Hardware/Software

  • Future Scope

  • Conclusion

Introduction to visdb

Introduction to VisDB

Typical difficulties faced with large databases:

  • Finding a specific data

  • No knowledge about database systems, query language and data model

  • Intersection data spots

  • 1 to 1 queries provide multiple data items with no feedback

Introduction to visdb1

Introduction to VisDB

  • Sorting the data items according to user query.

  • Visualizing as many data items as possible (Suppose in Ten Million) at the same time to give the user some kind of feedback on his query.

  • Also the resolution of current displays(1 to 3 million pixels) is an important consideration.

  • Interaction of the system with user.

Basic idea of visdb

Basic Idea of VisDB

  • Support Query Specification process by visually representing the result.

  • Restricts the visualized dimensions which are of no interest to users.

Basic idea of visdb1

Basic Idea of VisDB

  • Each pixel of screen is used to visualize the data items resulting from a query.

  • Approximate results are determined using distance functions.

  • These distances are then combined to get relevance factor which is useful for mapping.

Distance function

Distance Function

  • The distance between attribute and corresponding query value is determined.

  • Distance function used here are data type and application dependent.

  • In some cases, even for a single data type multiple distance functions can be used.

  • Calculating distance functions for

  • Number types(Integer) – Numerical difference.

  • Ordinal types(Grades) – domain specific distance functions

  • Nominal Types(Professionals) – Distance matrix

Combining distances into relevance factor

Combining Distances into Relevance Factor

  • Combine independently calculated distances of the different selection predicates.

  • But it should have a global meaning.

  • User interaction required.

    Obtain weighting factors (Wj, j Є 1, ……, #sp) as per order of importance from users.

  • Normalization of all distances.

    Linear transformation of the range [dmax,dmin] for each predicate

    e.g. (0,255)

Combining distances into relevance factor1

Combining Distances into Relevance Factor

  • For combining the normalized distances we use numerical mean functions such as :

    1. Weighted arithmetic mean for ‘AND’ – connected condition part.

  • Weighted geometric mean for ‘OR’- connected condition part.

    Relevance factor is inverse of distance value

Formula for calculating combined distance

Formula for calculating combined distance

Reducing the amount of data to be displayed

Reducing the amount of data to be displayed

  • Adequate heuristics are required to:

  • Reduce amount of data

  • Determine data items whose distances are to be displayed.

  • Hence α-quantile is defined as lowest value ξα such that:

Techniques used

Techniques Used

  • 3 techniques are used

  • Basic Visualization Technique

  • Mapping two dimensions to the Axes

  • Grouping the dimensions for each data Item

1 basic visualization technique

1. Basic Visualization Technique

  • Sorts data according to relevance with respect to query.

  • Then maps the relevance factors to colors.

  • Sorting is needed to avoid sprinkled images (which are not clear to user).

  • Highest Relevance factors centered to middle of window

  • Approximate answers create a rectangular spiral around this region(100% correct answers are yellow in color).

1 basic visualization technique1

1. Basic Visualization Technique

  • Color ranges from Yellow in middle to green, blue, red and lastly black

  • These ranges denote the distance from correct answers.

1 basic visualization technique2

1. Basic Visualization Technique

  • Multidimensional Visualization -

    In this we generate a separate window for each selection predicate of the query.

Question 1

Question 1:

  • 100% correct answers are denoted by which color in Basic Visualization Technique?

  • Red

  • Yellow

  • Green

  • White

  • Blue

Answer 1

Answer 1:

  • Correct answer: 2

2 mapping two dimensions to axes

2. Mapping Two Dimensions to Axes

  • Reasons for not pursuing 2D-3D visualizations although they are useful is because of

    • Limited Number of data items.

    • Systems already exist.

  • Improvement – Providing feedback on the direction of the distance into visualization.

2 mapping two dimensions to axes1

2. Mapping Two Dimensions to Axes

  • Assign two dimensions to the axes

  • Arrange the relevance factor according to the direction of the distance.

  • For 1 dimension, arrangement is

    Negative distances to left,

    Positive distances to right,

    For other dimension

    Negative distances to bottom,

    Positive ones to top

2d arrangement of 1dimension

2D arrangement of 1dimension

Problems in this method

Problems in this method

  • Corner of window would be completely empty.

  • Worst case- 2 diagonally opposite corners of the window may be completely empty which results in only half data items to be presented

  • Maximizing the number of data item conflict with arrangements that have multiple dimensions assigned to axis.

Question 2

Question 2:

  • In 1 Dimension Negative distances are arranged

  • 1) at the bottom

  • 2) to the right

  • 3) at the top

  • 4) to the left

Answer 2

Answer 2:

  • Correct answer: 4

3 grouping the dimensions for each data item

3. Grouping the Dimensions for each Data Item

  • All dimensions for one data item are grouped together in one area.

  • Visualizations generated using this arrangement consists of only one window.

  • We do not focus on shape to distinguish data items, and the criterion and arrangement of the data items is also different.

  • 2x2 pixels per dimension needed as opposed to 1 pixel per dimension in previous 2 methods.

Grouping arrangement for 5 dimensional data

Grouping arrangement for 5 Dimensional Data



  • Grouping arrangement is only suitable for focused search on smaller data sets because only one-fourth of the data items can be displayed on screen at one point of time.

  • But still provides more visualizations for data sets with larger dimensionality.

  • In other two techniques the pixels for each dimension of the data items are only related by their position.



  • Divided into the Visualization portion on left and Query Modification on right.

  • In Visualization portion the resulting data set including a certain percentage of approximate answers is displayed by using one of the visualization methods.

  • In Query Modification the sliders for modifying the selection predicates and weighting factors as well as some other options are provided.

Working contd

Working contd..

  • Different kind of sliders are there.

  • Ex: Sliders for numbers, sliders for discrete types, sliders for non-metric types(ordinal and nominal data types)

  • Other parameters listed are

    • Number of results

    • Query range

    • Weighting factors

    • Data values for selected tuple

    • Data values corresponding to some selected color range

Working contd1

Working contd..

  • Changing the percentage of data being displayed may completely change the visualization as distance values are normalized according to new range.

  • Normal Mode - System recalculates the visualization after each modification of query.

  • Auto-Recalculate Off mode – Queries are only recalculated on demand.

Question 3

Question 3:

  • In which two sections is VisDB mainly divided??

  • Visualization Portion

  • Grouping Dimentions

  • Query Modification

  • Coloration of Relevance factors

Answer 3

Answer 3:

  • Correct answer: 1 and 3

Question 4

Question 4

  • In which mode does the system recalculates the visualization after each modification of query?

  • Normal Mode

  • Auto Recalculate Mode

  • Visual Mode

  • None of the above.

Answer 4

Answer 4:

  • Correct answer: 1

Example 1000 data items

Example(1000 data Items)

Example 1000 data items1

Example(1000 data Items)

Example 7000 data items

Example(7000 data Items)

Example 7000 data items1

Example(7000 data Items)

Hardware software


  • Software used

    • C++

    • MOTIF

  • Hardware used

    • X- Windows on HP 7xx machines(Current version is main memory based and allows interaction data base exploration for database containing 50,000 data items)

Future scope

Future Scope

  • Automatic generation of queries that correspond to some specific region in one of the visualization windows.

  • Generate time series of visualizations corresponding to queries that are changed incrementally.

  • Applying to many different application domains each having its own parameters, distance functions, query requirements and so on.



  • This VisDB allows visualization of the largest amount of data that can be displayed at one point of time on current display.

  • Provides valuable feedback in querying the database

  • Allows the user to find results which would other wise remain hidden in database.

Visdb database exploration using multidimensional visualization

Thank you

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