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Mining Spatio-Temporal Datasets: Relevance, Challenges and Current Research Directions
Author(s) -
Tahar Kechadi,
Michela Bertolotto,
Filomena Ferrucci,
Sergio Di
Publication year - 2009
Language(s) - English
Resource type - Book series
DOI - 10.5772/6450
Subject(s) - relevance (law) , current (fluid) , data science , computer science , data mining , geography , political science , geology , oceanography , law
Spatio-temporal data usually records the states over time of an object, an event or a position in space. Spatio-temporal data can be found in several application fields, such as traffic management, environment monitoring, weather forerast, etc. In the past, huge effort was devoted to spatial data representation and manipulation with particular focus on its visualisation. More recently, the interest of many users has shifted from static views of geospatial phenomena, which capture its “spatiality” only, to more advanced means of discovering dynamic relationships among the patterns and events contained in the data as well as understanding the changes occurring in spatial data over time

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