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Full-Text Articles in Databases and Information Systems
A Hidden Markov Model For Matching Spatial Networks, Benoit Costes, Julien Perret
A Hidden Markov Model For Matching Spatial Networks, Benoit Costes, Julien Perret
Journal of Spatial Information Science
Datasets of the same geographic space at different scales and temporalities are increasingly abundant, paving the way for new scientific research. These datasets require data integration, which implies linking homologous entities in a process called data matching that remains a challenging task, despite a quite substantial literature, because of data imperfections and heterogeneities. In this paper, we present an approach for matching spatial networks based on a hidden Markov model (HMM) that takes full benefit of the underlying topology of networks. The approach is assessed using four heterogeneous datasets (streets, roads, railway, and hydrographic networks), showing that the HMM algorithm …
Evaluating Existing Manually Constructed Natural Landscape Classification With A Machine Learning-Based Approach, Rok Ciglic, Erik Strumbelj, Rok Cesnovar, Mauro Hrvatin, Drago Perko
Evaluating Existing Manually Constructed Natural Landscape Classification With A Machine Learning-Based Approach, Rok Ciglic, Erik Strumbelj, Rok Cesnovar, Mauro Hrvatin, Drago Perko
Journal of Spatial Information Science
Some landscape classifications officially determine financial obligations; thus, they must be objective and precise. We presume it is possible to quantitatively evaluate existing manually constructed classifications and correct them if necessary. One option for achieving this goal is a machine learning method. With (re)modeling of the landscape classification and an explanation of its structure, we can add quantitative proof to its original (qualitative) description. The main objectives of the paper are to evaluate the consistency of the existing manually constructed natural landscape classification with a machine learning-based approach and to test the newly developed general black-box explanation method in order …
Discovery Of Topological Constraints On Spatial Object Classes Using A Refined Topological Model, Ivan Majic, Elham Naghizade, Stephan Winter, Martin Tomko
Discovery Of Topological Constraints On Spatial Object Classes Using A Refined Topological Model, Ivan Majic, Elham Naghizade, Stephan Winter, Martin Tomko
Journal of Spatial Information Science
In a typical data collection process, a surveyed spatial object is annotated upon creation, and is classified based on its attributes. This annotation can also be guided by textual definitions of objects. However, interpretations of such definitions may differ among people, and thus result in subjective and inconsistent classification of objects. This problem becomes even more pronounced if the cultural and linguistic differences are considered. As a solution, this paper investigates the role of topology as the defining characteristic of a class of spatial objects. We propose a data mining approach based on frequent itemset mining to learn patterns in …