Download VisDB:Multidimensional Data Exploration Tool

VisDB: Database Exploration Using
Multidimensional Visualization
Maithili Narasimha
4/24/2001
VisDB
Scientific and Geographic databases tend to
have large amounts of data.
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Some of the challenges in dealing with these
databases are:
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Mining these databases for useful information is a
difficult task due to the sheer volume of data
VisDB
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Users do not know what they are looking for
exactly.
With traditional query specification languages, it is
not possible to specify vague queries and thus not
possible to get approximate results.
There is no feedback. Result set may contain too
few or too many points.
VisDB
Requirements for a good Visualization
System to explore large databases:
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Flexible Query Specification
Good Query Feedback
Interactive system
VisDB
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Also, the users should be able to view
as many data points as possible to see
the patterns and clusters.
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Display size and resolution are limiting factors
Also necessary to display the
interdependencies between data
attributes, Hotspots(anomalies).
VisDB
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The goal of the VisDB system is to
address the tasks of visualization of the
results , and that of incrementally
refining the query to provide an
effective way to find interesting data
properties.
VisDB
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The approach.
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Use each pixel of the screen to visualize
the results.
Provide data items not only fulfilling the
result exactly , but also those that match
approximately.
VisDB
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Approximate results are determined by
a relevance factor.
The relevance factor of a data item is
obtained by calculating distances for
each selection predicate and combining
them.
The more the combined distance, the
less the relevance of the data point.
Calculating the Relevance Factor
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Calculate the distance.
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Combining distances.
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Simple for Quantitative data.
Nominal and Ordinal?
User and Query dependent.
Weighting Factor for each attribute.
Normalizing.
Arithmetic Mean for AND and Geometric mean for
OR for combining different condition parts.
Relevance Factor is the inverse of the
Combined distance.
VisDB
Basic Visualization Technique
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Sort the data points according to their
relevance, with respect to the query.
Assign colors depending on the
relevance.
Plot the sorted, colored points starting
from the center of the screen moving
outwards in a rectangular spiral fashion.
Overall Result Plotting
VisDB
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To relate the visualization of the overall result
to the visualization of different selection
predicates, separate windows for each
selected predicate of the query are created
and shown along with the result window.
The position of the data items in all the other
windows is determined by their position in
the overall result window.
Arrangement of Windows for 5D Data
VisDB
Mapping two dimensions to the axes
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It is possible for the user to assign two
attributes to the axes and the system will
arrange the relevance factors according to the
directions of the distance of the data point from
the selection predicate.
With this method it is possible to provide better
feedback to the user.
However, we may not be able to use the display
efficiently in some cases (I.e. some quadrants
may not be used fully, while others are
saturated)
2D Representation
VisDB
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Grouping the dimensions
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The pixels corresponding to the different
dimensions of one data item are placed in
one area instead of distributing them in
different windows.
Will require more pixels per dimension per
data item.
May provide more useful visualizations for
data sets with larger dimensionality.
Grouping multi dimensional data
VisDB
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Interactive data exploration
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Users initially specify their queries, using some
query language.
Inside the VisDB interactive query and
visualization interface, it is possible to view the
visualizations and perform query modifications.
System provides sliders for modifying selection
predicates, weight factors and other options.
VisDB
VisDB
Conclusion
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Useful for identifying and isolating clusters,
correlations and hotspots in large
databases.
Good Query specification system.
No Zoom or pan for the visualizations.