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View - and Scale-Based Progressive Transmission of Vector Data

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1 View- and Scale-Based Progressive Transmission of Vector Data Padraig Corcoran, Peter Mooney, Adam Winstanley and Michela Bertolotto. Department of Computer Science, National University of Ireland Maynooth. School of Computer Science and Informatics, University College Dublin
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View- and Scale-Based Progressive Transmission of Vector Data

Padraig Corcoran, Peter Mooney, Adam Winstanley and Michela Bertolotto.

Department of Computer Science,National University of Ireland Maynooth.

School of Computer Science and Informatics,University College Dublin

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Introduction

● Web application development is in the middle of a paradigm shift.

● Web-GIS applications still linger behind desktop-GIS in terms of:● Functionality.● Interface.● User Interaction.

● This can be attributed to the manner in which spatial data is transmitted.

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Tile-based Transmission

● Predominant transmission methodology● Vector data converted to raster maps tiles on the

server.● Map tiles transmitted to client.● Used by Google Maps and OpenStreetMap.

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● Advantages:● HTML has native support for images.● Image compression is an advanced science.● All data requests are pre-computed.

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● Disadvantage:● Vector data is not transmitted therefore the client

cannot perform spatial queries or adapt the visualization.

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Vector-Based Transmission

● Can we transmit vector data and maintain the advantages of tile-based transmission?

● Development of such technology is a main goal in the field of Progressive Transmission.

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Progressive Transmission

● For large data sets a trade off exists between:● Transmission of high levels of detail.● Transmission in reasonable time.

● Progressive transmission attempts to optimize this trade-off for vector data.

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● Progressive transmission is characterized by two properties:● Data is transmitted in the form of increments or

refinements.● To reduce redundancy data is not re-transmitted.

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View- and Scale Based Transmission

● In order to structure existing research in this field we propose a classification.

● All methods for progressive transmission may be classified as view- or scale-based.

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View-Based Transmission

● Data is transmitted progressively as a function of changing viewing window.

Time (Progressively Changing View)

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Scale-Based Transmission

● Data is transmitted progressively as a function of changing scale.

Time (Progressively Changing Scale)

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Scale-Based Implementation

● Refinement is the inverse of generalization.● All refinements are actually generalizations and

therefore satisfy the same objectives.

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Fusion View- and Scale-Based

● Both approaches reduce the volume of data transmitted in different ways.

● To maximise efficiency concepts from both must be fused.

● Currently the most advanced fusion method is that of Li et al. 2009

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Li et al. Methodology

● The vector data is divided into tiles.● The subset of tiles a user views is determined.● Each of these tiles is then transmitted using a

scale based transmission strategy.

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● Disadvantages:● Features which span multiple tiles must be

segmented and rejoined.● Such features cannot be generalized.

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Proposed Fusion Methodology

● A transmission method which removes the requirement for tiles is proposed.

● Firstly all features are generalized in a manner which maintains topology (Corcoran et. al, IJGIS 2011).

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● Features are then inserted into an R-tree (spatial indexing method).

● Given a viewing window the features contained within this window are progressively transmitted while maintaining topology (Corcoran. et al, Agile 2011).

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Implementation

● Implemented using client server model.● Server client communication uses HTML 5

WebSocket API.● Client rendering uses HTML 5 Canvas API.

Sequence Diagram

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Transmission Example

Large Scale Map

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Quantitative Results

Comparison of data volume transmitted.

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Conclusions

● We provide an analysis and propose a framework to classify existing progressive transmission methods.

● Subsequently, a new fusion method is proposed.

● Request are computed on the fly; future work will aim to reduce computational complexity.


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