POP-11: a practical language for artificial intelligence
POP-11: a practical language for artificial intelligence
Structural Stereopsis for 3-D Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object-oriented modeling and design
Object-oriented modeling and design
Map integration—update propagation in a multi-source environment
GIS '97 Proceedings of the 5th ACM international workshop on Advances in geographic information systems
Relational Matching
Model independent assertions for integration of heterogeneous schemas
The VLDB Journal — The International Journal on Very Large Data Bases
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
Ontology-Based Geographic Data Set Integration
STDBM '99 Proceedings of the International Workshop on Spatio-Temporal Database Management
A Model-Based, Open Architecture for Mobile, Spatially Aware Applications
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Efficient integration of road maps
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
Generalization of land cover maps by mixed integer programming
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
Integrating data from maps on the world-wide web
W2GIS'06 Proceedings of the 6th international conference on Web and Wireless Geographical Information Systems
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In order to solve spatial analysis problems, nowadays a huge amount of digital data sets can be accessed: cadastral, topographic, geologic, and environmental data, in addition to all kinds of other types of thematic information. In order to fully exploit and combine the advantages of each data set, they have to be integrated. This integration has to be established at an object level leading to a multiple representation scheme. Depending on the type of data sets involved, it can be achieved using different techniques.Such a linking has many benefits. First, it helps to limit redundancies and inconsistencies. Furthermore, it helps to take advantage of the characteristics of more than one data set and therefore greatly supports complex analysis processes. Also, it opens the way to integrated data and knowledge processing using whatever information and processes are available in a comprehensive manner. This is an issue currently addressed under the heading of ‘interoperability’.Linking has basically two aspects: on the one hand, the links characterize the correspondence between individual objects in two representations. On the other hand, the links also can carry information about the differences between the data sets and therefore have a procedural component, allowing the generation of a new data set based on given information (i.e., database generalization).In the paper three approaches for the linking of objects in different spatial data sets are described. The first defines the linking as a matching problem and aims at finding a correspondence between two data sets of similar scale. The two other approaches focus on the derivation of one representation from the other one, leading to an automatic generation of new digital data sets of lower resolution. All the approaches rely on methodologies and techniques from artificial intelligence, namely knowledge representation and processing, search procedures, and machine learning.