1 ACM GIS 2007 An Interactive Framework for Raster Data Spatial Joins Wan Bae (Computer Science, University of Denver) Petr Vojtěchovský (Mathematics, University of Denver) Shayma Alkobaisi (Computer Science, University of Denver) Scott T. Leutenegger (Computer Science, University of Denver) Seon Ho Kim (Computer Science, University of Denver)
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An Interactive Framework for Raster Data Spatial Joins
An Interactive Framework for Raster Data Spatial Joins. Wan Bae (Computer Science, University of Denver) Petr Vojtěchovský (Mathematics, University of Denver) Shayma Alkobaisi (Computer Science, University of Denver) Scott T. Leutenegger (Computer Science, University of Denver) - PowerPoint PPT Presentation
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1 ACM GIS 2007
An Interactive Framework for Raster Data Spatial Joins
Wan Bae (Computer Science, University of Denver)
Petr Vojtěchovský (Mathematics, University of Denver)
Shayma Alkobaisi (Computer Science, University of Denver)
Scott T. Leutenegger (Computer Science, University of Denver)
Seon Ho Kim (Computer Science, University of Denver)
2 ACM GIS 2007
Outline
Introduction
Issues and Problems
Probabilistic Joins
Sampling Joins
Interactive Framework
Experiments
Conclusion
3 ACM GIS 2007
Geographic Information Systems
Web applicationWeb application
datadata datadata
datadata
• CollectCollect• StoreStore• RetrieveRetrieve
• Integration of georeferenced dataIntegration of georeferenced data• Spatial queriesSpatial queries• Complex spatial data analysis & Complex spatial data analysis & modeling for decision supportmodeling for decision support
GIS
Web application
UsersUsers
datadata
datadatadatadata
4 ACM GIS 2007
Raster Data Model
(a) Satellite Image
0 1 2 3 4 5 6 7 8 90 R T1 R T2 H R3 R4 R R5 R6 R T T H7 R T T8 R9 R
(b) Raster Model
• A great portion of georeferenced data• Simple data structure but greater storage space• Continuously changing data
5 ACM GIS 2007
Continuously Changing Data
6 ACM GIS 2007
Raster Data Spatial Joins
(a) (b)
“Find the regions where rainfall rate is greater than 1.0 and wind speed is greater than 50”
7 ACM GIS 2007
Issues for User-driven Data Exploration
Fast Query response time
– Time consuming for exact answers due to large size of data sets
– Time intensive GIS decision support queries
– Lack of optimization and approximation techniques for raster data joins
Interactive query processing
– Lack of interactivities in traditional GIS
– No user control over query processing Visualization increases the utility of the GIS
8 ACM GIS 2007
Our Approach
Fast approximation of query results
1. probabilistic join
2. sampling join
Visualize intermediate results
1. “big picture” of query result
2. partial result: non-blocking joins
Allow users to control query processing
For faster and more effective decision support queries:
9 ACM GIS 2007
Our Approximations
2. Can use the result of a subset of data cell joins for the final answer?
R (8/16) S (9/16) = they must join!
1. What is the probability that R joins S?
1 joins / 2 cells ? / 16 cells
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ACM GIS 2007
Augmented Quad-trees
Both data sets are indexed using Quad-trees
NW
SESW
NE NW
SESW
NE
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ACM GIS 2007
Join Probability
Let X = [0, 1], m and n be randomly chosen intervals in X of length a, b. The probability p that m ∩ n ≠ 0