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U.S. Department of the Interior U.S. Geological Survey Center of Excellence in Geospatial Information Science eb-client Based Distributed eneralization and Geoprocessing Eric B. Wolf and Kevin Howe 5 February 2009

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Page 1: U.S. Department of the Interior U.S. Geological Survey Center of Excellence in Geospatial Information Science Web-client Based Distributed Generalization.

U.S. Department of the InteriorU.S. Geological SurveyCenter of Excellence in Geospatial Information Science

Web-client Based DistributedGeneralization and GeoprocessingWeb-client Based DistributedGeneralization and Geoprocessing

Eric B. Wolf and Kevin Howe

5 February 2009

Page 2: U.S. Department of the Interior U.S. Geological Survey Center of Excellence in Geospatial Information Science Web-client Based Distributed Generalization.

Overview

Architectures for generalization

Advantages of web-client generalization

Our experiment

Results

Summary

Architectures for generalization

Advantages of web-client generalization

Our experiment

Results

Summary

Page 3: U.S. Department of the Interior U.S. Geological Survey Center of Excellence in Geospatial Information Science Web-client Based Distributed Generalization.

Architectures - Monolithic

World WideWeb

World WideWeb

Page 4: U.S. Department of the Interior U.S. Geological Survey Center of Excellence in Geospatial Information Science Web-client Based Distributed Generalization.

Architectures - MRDBs

Page 5: U.S. Department of the Interior U.S. Geological Survey Center of Excellence in Geospatial Information Science Web-client Based Distributed Generalization.

Architectures - WPS

World

Wide

Web

World

Wide

Web

Page 6: U.S. Department of the Interior U.S. Geological Survey Center of Excellence in Geospatial Information Science Web-client Based Distributed Generalization.

Architectures – Client Side

Web Browser

World

Wide

Web

World

Wide

Web

JavascriptJavascript

Page 7: U.S. Department of the Interior U.S. Geological Survey Center of Excellence in Geospatial Information Science Web-client Based Distributed Generalization.

Advantages of Client-side Generalization

Reduced software costs

Reduced software complexity

Resolves conflation issues

Reduced software costs

Reduced software complexity

Resolves conflation issues

Page 8: U.S. Department of the Interior U.S. Geological Survey Center of Excellence in Geospatial Information Science Web-client Based Distributed Generalization.

Our Experiment: Is it possible?

Douglas-Peucker algorithm

Initially implemented inside OpenLayers

Broke out code for benchmarking

Multiple datasets – synthetic and typical

Multiple browser environments

Douglas-Peucker algorithm

Initially implemented inside OpenLayers

Broke out code for benchmarking

Multiple datasets – synthetic and typical

Multiple browser environments

How does performance compare to other cyberinfrastructure models?

How does performance compare to other cyberinfrastructure models?

Page 9: U.S. Department of the Interior U.S. Geological Survey Center of Excellence in Geospatial Information Science Web-client Based Distributed Generalization.

Recognized best for linear generalization

Typically O(n2) in complexity

Naturally recursive

Well documented

Recognized best for linear generalization

Typically O(n2) in complexity

Naturally recursive

Well documented

About the Douglas-Peucker algorithm

Page 10: U.S. Department of the Interior U.S. Geological Survey Center of Excellence in Geospatial Information Science Web-client Based Distributed Generalization.

SyntheticSynthetic

Datasets Selected

TypicalTypical

Page 11: U.S. Department of the Interior U.S. Geological Survey Center of Excellence in Geospatial Information Science Web-client Based Distributed Generalization.

Browsers Compared

Paper Presentation

Browser Version Build Version Build

Mozilla Firefox

2.0.0.16 1.8.1.14 2.0.0.20 1.8.1.20

3.0.1 1.9.0.1 3.0.5 1.9.0.5

3.1 1.9.1b1pre 3.1b2 1.9.1b2

Microsoft Internet Explorer

7.0 5730.11 7.0 5730.13

8.0 beta 26001.18241

8.0 beta 26001.18241

Apple Safari 3.1.2 525.21 3.2.1 525.27.1

Opera 9.5.2 10108 9.6.3 10476

Google Chrome0.2.149.29

1798 1.0.154.43

Page 12: U.S. Department of the Interior U.S. Geological Survey Center of Excellence in Geospatial Information Science Web-client Based Distributed Generalization.

Results

0

100

200

300

400

500

600

700

800

900

1000

Arr

ay

LL

ist

Arr

ay

LL

ist

Arr

ay

LL

ist

Arr

ay

LL

ist

Arr

ay

LL

ist

Arr

ay

LL

ist

Arr

ay

LL

ist

Arr

ay

LL

ist

FF 2 FF 3 FF 3.1 Chrome Safari Opera IE 7 IE 8

Browser

Gen

eral

izat

ion

rate

(ms / 1

000

vert

ices

)

streams

roads

fjord (b)

fjord (a)

Page 13: U.S. Department of the Interior U.S. Geological Survey Center of Excellence in Geospatial Information Science Web-client Based Distributed Generalization.

DiscussionEnvironment difficult to benchmark

Data management imposes processing demands depending on data structure

Safari and Chrome handled arrays as expected

ECMAScript interpreters are quickly evolving

Except for Microsoft – 2/3rds of internet users

Performance of IE8 Beta on par with Firefox 2.0

When it manages to run at all…

Environment difficult to benchmark

Data management imposes processing demands depending on data structure

Safari and Chrome handled arrays as expected

ECMAScript interpreters are quickly evolving

Except for Microsoft – 2/3rds of internet users

Performance of IE8 Beta on par with Firefox 2.0

When it manages to run at all…

Page 14: U.S. Department of the Interior U.S. Geological Survey Center of Excellence in Geospatial Information Science Web-client Based Distributed Generalization.

SummaryIs client-side geoprocessing possible? Yes

Challenging development environment

Interpreters not robust

Poor stack management - avoid recursion!

Arrays commonly handled like hashes

Standards not equally implemented

Microsoft Internet Explorer commonly fails

How does it compare?

Computation time less than network latency

Is client-side geoprocessing possible? Yes

Challenging development environment

Interpreters not robust

Poor stack management - avoid recursion!

Arrays commonly handled like hashes

Standards not equally implemented

Microsoft Internet Explorer commonly fails

How does it compare?

Computation time less than network latency

Page 15: U.S. Department of the Interior U.S. Geological Survey Center of Excellence in Geospatial Information Science Web-client Based Distributed Generalization.

Future Directions

Integrate client-based generalization with WFS for multiscale features

Determine what geoprocessing operations are best handled as a WPS or client-based?

Create a library of geoprocessing methods

Integrate client-based generalization with WFS for multiscale features

Determine what geoprocessing operations are best handled as a WPS or client-based?

Create a library of geoprocessing methods

Page 16: U.S. Department of the Interior U.S. Geological Survey Center of Excellence in Geospatial Information Science Web-client Based Distributed Generalization.

Questions?


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