TY - JOUR

T1 - Linking objects of different spatial data sets by integration and aggregation

AU - Sester, Monika

AU - Anders, Karl Heinrich

AU - Walter, Volker

N1 - Funding information: Parts of this work is embedded in the joint research project Semantic Modelling for the Extraction of Spatial Objects from Images and Maps and is funded by the German Research Organization (DFG). We want to thank our anonymous reviewers for their valuable comments and also the editors for their encouragement and help.

PY - 1998/12

Y1 - 1998/12

N2 - In order to solve spatial analysis problems, nowadays a huge amount of digital data sets can be assessed: cadastral, topographic, geologic, and environmental data, in addition to all kinds of other types of thermatic 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.

AB - In order to solve spatial analysis problems, nowadays a huge amount of digital data sets can be assessed: cadastral, topographic, geologic, and environmental data, in addition to all kinds of other types of thermatic 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.

KW - Aggregation

KW - Database generalization

KW - Machine learning

KW - Matching

KW - Multiple representations

UR - http://www.scopus.com/inward/record.url?scp=0032410370&partnerID=8YFLogxK

U2 - 10.1023/A:1009705404707

DO - 10.1023/A:1009705404707

M3 - Article

AN - SCOPUS:0032410370

VL - 2

SP - 335

EP - 358

JO - GeoInformatica

JF - GeoInformatica

SN - 1384-6175

IS - 4

ER -