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.
Mining Spatio-Temporal Datasets: Relevance, Challenges and Current Research Directions / M., Tahar Kechadi; Michela, Bertolotto; Filomena, Ferrucci; DI MARTINO, Sergio. - STAMPA. - (2009), pp. 215-228. [10.5772/6450]
Mining Spatio-Temporal Datasets: Relevance, Challenges and Current Research Directions
DI MARTINO, SERGIO
2009
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.