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G. Mountrakis, P. Agouris and A. Stefanidis, “Similarity Learning in GIS: An Overview of Definitions, Prerequisites and Challenges,” In: M. Vassilakopoulos, A. Papadopoulos and Y. Manolopoulos, Eds., Spatial Databases: Technologies, Techniques and Trends, Idea Group Inc., Calgary, 2004, pp. 294-321. doi:10.4018/978-1-59140-387-6.ch013

has been cited by the following article:

• TITLE: Moving towards Personalized Geospatial Queries

  AUTHORS: Giorgos Mountrakis, Anthony Stefanidis

  KEYWORDS: Geospatial Databases, Geographic Information Systems, Geospatial Queries, Similarity Learning, Preference Modeling, Adaptive Systems, Digital Government

  JOURNAL NAME: Journal of Geographic Information System, Vol.3 No.4, October 19, 2011

  ABSTRACT: Geospatial datasets are typically available as distributed collections contributed by various government or commercial providers. Supporting the diverse needs of various users that may be accessing the same dataset for different applications remains a challenging issue. In order to overcome this challenge there is a clear need to develop the capabilities to take into account complicated patterns of preference describing user and/or application particularities, and use these patterns to rank query results in terms of suitability. This paper offers a demonstration on how intelligent systems can assist geospatial queries to improve retrieval accuracy by customizing results based on preference patterns. We outline the particularities of the geospatial domain and present our method and its application.