Production matching for large learning systems
Production matching for large learning systems
A processing pipeline for X3D earth-based spatial data view services
Proceedings of the 14th International Conference on 3D Web Technology
Hierarchical program representation for program element matching
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
A mismatch description language for conceptual schema mapping and its cartographic representation
GIScience'10 Proceedings of the 6th international conference on Geographic information science
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With the success of applications providing geospatial 3D data to end-users via virtual globes and web-based services, the need for accessing more accurate and up-to-date information has come up. Geodata typically comes from different and unrelated sources, whereas users expect a seamless view which respects their specific needs. A modern geo portrayal service therefore has to deliver information from heterogeneous data sources adapted to specific applications. Each data model introduces limitations that might make it harder to fulfil user requirements. Since in practise user requirements constantly change and data sets are updated, the complexity of configuring and adminstering such a process increases disproportionately to both the number of requirements and data model limitations. To overcome this situation, we outline an approach which seeks to avoid what we call the explosion of interdependencies between formalized requirements and geodata features. Our goal is to keep original data virtually untouched while delivering adapted data to multiple users with specific portrayal requirements. We therefore introduce an intermediate representation (IR) to alleviate the growing complexity of interdependencies from O(n · m) to O(n + m). We adapt this technique to the geospatial domain, deriving a prototypical implementation of an OGC Web 3D Service which delivers X3D documents. The service is capable of adapting to user requirements and at the same time serving originally heterogeneous geodata. We introduce the implementation, discuss results and application opportunities.