Rimma V. Nehme

1
SCUBA Scalable Cluster-Based Algorithm for
Evaluating Continuous Spatio-Temporal Queries on
Moving Objects

• Rimma V. Nehme
• Department of Computer Sciences,
• Purdue University,
• W.Lafayette, IN 47906 USA
• rnehme_at_cs.purdue.edu

Elke A. Rundensteiner Department of Computer
Sciences, Worcester Polytechnic Institute,
Worcester, MA 01609 USA rundenst_at_cs.wpi.edu
2
Outline

• Motivation
• Related Work
• Our Approach SCUBA
• Experimental Study
• Conclusion

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Challenges for Continuous Query Processing on
Spatio-Temporal Data Streams
- moving objects
- dynamic range query
- dynamic kNN query

• Scalability
• Large number of objects
• Large number of queries
• Limited Resources
• Memory
• CPU
• Real-time Response
• Requirement

Novel Idea Exploit the fact that objects
naturally move in groups (i.e., clusters) to
optimize query evaluation
The challenge is to provide fast query response
in update-intensive environments
4
Motivation
Monitor the traffic in the red areas
Continuously return the area covered by the heard
during the migration
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Big Picture

• Shared Execution

• SINA MXA04
• SEA-CNN XMA05
• Q-Index PXK02
• PSoup CF03
• NiagaraCQ CDT00

Novel Idea
We use clustering as means to improve execution
of spatio-temporal queries on moving objects
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Our Idea Moving Clusters
Continuously retrieve closest police car next to
me

• Main Idea Abstracting individual entities into a
  cluster based on common attributes
• - Direction
• - Speed
• - Spatial Position
• With cluster abstractions,
• we want to minimize the number of unnecessary
  individual object/query joins, thus optimizing
  query evaluation

Police Car
Scalable Cluster-Based Algorithm for Evaluating
Continuous Spatio-Temporal Queries on Moving
Objects (SCUBA)
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Advantage of Moving Clusters
If two abstractions do not overlap' then we can
discard negative candidates and avoid individual
joins.

• When clusters dont overlap, we avoid many joins
  of individual objects within those clusters

m1
m2
No need to join objects/queries in m1 with
queries/objects in m2
- Moving object
- Spatio-temporal range query
We present SCUBA in the context of continuous
spatio-temporal range queries
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Advantage of Moving Clusters

• Objects/Queries continuously move   
• Grid cells are static
• If put in grid, we have to continuously have to
  take them and put into and out of grid cells.
• Instead  we want to make "flexible cells"  i.e.,
  moving clusters

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Architecture Overview

• SCUBA-enabled motion operator execution
• Answers produced periodically (every ?)

CAPE
Grid-based Join Between/Within Clusters
? Time interval expires
Results Data Stream
-moving object
SCUBA - Motion Operator
-range query
Moving Clusters
Moving Queries Data Stream
Moving Objects Data Stream
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Network Constrained Movement
Movement is constrained within road network
Roads edges Intersections connection nodes
Connection Node (CNLoc)
New York City
SCUBA supports both constrained and unconstrained
movement.
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Moving Cluster Representation in SCUBA
Cluster members moving objects
Max Cluster Size
Centroid
Actual Cluster Size
TD
Moving clusters expire after some time
Cluster members moving queries
Direction Vector
Cluster Member Representation Inside Cluster
Cluster member (moving object)
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SCUBA Execution

• SCUBA produces result periodically (every ? time
  units)

• SCUBA has three phases

• Phase I Cluster Pre-Join Maintenance
• Formation of new clusters
• Dissolving empty clusters
• Expanding existing clusters

Cluster Pre-Join Maintenance
Cluster-Based Joining
Cluster Post-Join Maintenance
DONE
? Timeout
DONE

• Phase II Cluster-Based Joining
• Joining clusters
• Joining objects and queries inside clusters

DONE

• Phase III Cluster Post-Join Maintenance
• Dissolving expiring clusters
• Relocating non-expiring clusters based on
  velocity vector

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Phase I Cluster Pre-Join Maintenance

• Clustering is done incrementally (upon the
  arrival of updates)
• Location update format
• (ID, Loct, t, Speed, CNLoc, ...)
• Use 2 thresholds destination
• TD distance threshold
• TS speed threshold
• Destination

Clustering New Object Example
M1
M1
M2
M2
(1) New moving object arrives
(2) Hash object into grid
Parent Cluster
(4) If cluster has expanded check for overlap
with neighboring cells (make new entries if
necessary)
(3) Add object to cluster and update cluster
attributes
-centroid position -radius -average speed -member
count
(5) If object left existing cluster, for a new
cluster and old cluster is empty, dissolve old
cluster.
Clustering Algorithm is based on Leader-Follower
Clustering Algorithm (J.A. Hartigan. Clustering
Algorithms, John Wiley and Sons 1975)
M3
M3
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Phase II Cluster-Based Joining

• Location Updates
• Arrival

Incremental Clustering
Cluster-Based Join
Phase II
Cluster-Based Join
1. Join-Between
2. Join-Within
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Phase II Cluster-Based Joining

• Join-Between
• Between two clusters
• Join-Within
• For each cluster (joining objects and queries
  inside)
• For two overlapping clusters (cross-join between
  objects and queries from the two clusters)

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Phase III Cluster Post-Join Maintenance

• Clear the grid
• Dissolve expiring clusters
• Relocate non-expiring clusters based on
  velocity vector back into the grid

Insert into the grid
New Cluster Position Updated
Dissolved
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Moving Cluster-Based Load Shedding

• Load Shedding - process of dropping excess load
  from the system when the demand on resources is
  above the system capacity TCZ03.
• Load shedding reduces resource requirements by
  dropping data, thereby sacrificing the accuracy
  of the query answers.
• The main goal is to minimize the degradation in
  accuracy.

Focus Discarding data inside moving clusters
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Experimental Settings

• Implemented inside Java-based CAPE streaming
  system RDZ05
• Used Network-based Generator of Moving Objects
  BR02 to generate a set of moving objects and
  moving queries in Worcester County (Tiger Line
  files)
• Unless mentioned otherwise, the following are the
  parameters used
• 10,000 moving objects and 10,000 moving queries.

• Clustering Thresholds
• TD 100 (spatial units),
• TS 10 (spatial units/time units)
• TN 0 (no load shedding)
• Grid 100x100

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Experimental Results

• From Dissimilar to Similar Motion
• Higher skew factor means more dense objects and
  queries (i.e., more clusterable)
• Compare against regular grid-based execution
  (termed REGULAR)

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Experimental Results

• Incremental vs. Non-incremental

• Join time slightly improves with non-incremental
  clustering
• But clustering wait time outweighs advantage of
  faster join

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Experimental Results

• Varying Grid Cell Size

- Performance of regular grid-based execution
improves with finer granularity of grid cells
(But memory requirements increase as well)
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Experimental Results

• Cluster Maintenance
• Cluster maintenance time is cheap relative to
  the join time

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Conclusions

1. Designed SCUBA is a novel cluster-based algorithm
   for continuously evaluating spatio-temporal
   queries.
2. Scalability in SCUBA is achieved through shared
   cluster-based execution.
3. Implemented SCUBA in CAPE streaming database
4. Experimental results show that SCUBA outperforms
   regular grid-based indexing scheme when executing
   on densely moving objects
5. Clustering significantly improves performance
   when processing densely moving objects
6. Maintaining clusters (overhead) is very small

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Future Work

• Non-circular clusters
• Extend to other types of spatio-temporal queries
• CKNN
• Aggregate
• Hierarchical clustering (merge and break-down
  clusters)

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Thank you.
Mass Pike in Boston Satellite Image, Google Maps
2006
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Additional Slides
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References

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Data Structures

• Objects Table
• Queries Table
• ClusterHome Table
• ClusterStorage Table
• ClusterGrid

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