An Approach to Active Spatial Data Mining

1
An Approach to Active Spatial Data Mining
STING

• Wei Wang
• Data Mining Lab, UCLA
• March 24, 1999

2
Outline

• Introduction
• Spatial Data Mining Triggers
• Strategies
• Performance
• Conclusions

3
Introduction

• Huge amount of spatial data are generated
  everyday.
• Earth Observing System
• National Spatial Data Infrastructure
• National Image Mapping Agency
• one meter resolution data
• digital earth
• Users are usually interested in the hidden
  information.
• Aggregate information
• Clustering
• Patterns

4
Introduction

• Knowledge discovery processes are computationally
  expensive.
• Todays technology advances provide necessary
  computing power to carry out such complicated
  processes.

5
Spatial Data mining Triggers

• Since data evolves over time, interesting
  patterns are likely to emerge or change.
• Goal identify and find (most of) interesting
  patterns
• Problems
• Knowledge discovery processes are expensive.
• It is not feasible to re-process the entire
  data set for every change.
• Approach to periodically examine the data.
• Long delays
• Transient patterns might be missed
• Natural solution Usage of triggers.

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Spatial Data mining Triggers

• Traditional database triggers can not be directly
  applied
• Expressive power of traditional database triggers
  is limited, especially in describing spatial
  relationships.
• Example Trigger bandwidth reallocation when the
  size of a cluster exceeds 20.

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Strategies

• STING was designed to introduce and support
  spatial triggers efficiently.
• Observation (spatial locality) Only objects
  added to the shaded area will contribute to the
  growth of cluster size at this moment.

8
Strategies

• STING Strategy Monitor only the area occupied
  by potential clusters and its neighborhood.
• Observation (cumulative effect) at least 4 more
  objects are needed in order to make the cluster
  size be 20.
• STING Strategy Space is organized in a
  hierarchy so that updates can be suspended at
  some level in the hierarchy until the cumulative
  effect might cause the trigger to be fired.

9
Strategies

• Space is recursively divided into smaller
  rectangular cells down to a specified granularity
  and is organized via the inherit pyramid
  hierarchy.

Level 1
10
Strategies

• STING decomposes a trigger into a set of
  sub-triggers associated with individual cells in
  the hierarchical structure to monitor the
  cumulative effect of data changes within the cell.

Sub-trigger on cell
Higher level sub-trigger on cell
11
Strategies

• Updates/insertions are suspended at some level in
  the hierarchy until such time that the cumulative
  effect of these insertions might cause the
  trigger condition to become satisfied.

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Level 1
Level 0
12
Strategies
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Level 3
Level 2
No update of cluster !
13
Performance

• Comparison with periodic re-examination
• If the period is set to be less than 4000
  updates, STING consumes less CPU cycles.
• Significant delay and transient patterns misses
  can occur for larger period.
• Not acceptable in many applications
• No delay and no transient patterns missed with
  STING.

14
Conclusions

• STING is an efficient approach to active spatial
  data mining.
• Users can define triggers to monitor the change
  of spatial data.
• Updates to the data are carefully monitored
• Evaluation of trigger is postponed until the
  cumulative effect of data updates might cause the
  trigger to be fired.