Abstract
In these last years, large quantity of spatio-temporal data stored leads to new needs such that management of natural risks, health or anthropogenic (e.g. understanding the dynamic of dengue epidemic). In this paper, we define a new theoretical framework for extracting spatio-sequential patterns. A spatio-sequential pattern is a sequence representing evolution of locations and their neighborhoods over time. We propose an efficient algorithm based on depth-first-search with successive projections over the database. We introduce a new interestingness measure taking into account both spatial and temporal aspects. Experiments are conducted on real datasets highlight the relevance of our method.
Original language | English |
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Pages | 161-172 |
Number of pages | 12 |
State | Published - 1 Dec 2012 |
Externally published | Yes |
Event | Revue des Nouvelles Technologies de l'Information - Duration: 1 Dec 2012 → … |
Conference
Conference | Revue des Nouvelles Technologies de l'Information |
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Period | 1/12/12 → … |