Abstract
Periodic behaviors are an important component of the life of most living species. Daily, weekly, or even yearly patterns are observed in both human and animal behaviors. These behaviors are searched as frequent periodic episodes in event streams. We propose an efficient algorithm for the discovery of frequent and periodic episodes. Update procedures allow us to take into account that behaviors also change with time, or because of external factors. The interest of our approach is illustrated on two real datasets.
Cite
CITATION STYLE
Soulas, J., & Lenca, P. (2015). Periodic episode discovery over event streams. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9273, pp. 547–559). Springer Verlag. https://doi.org/10.1007/978-3-319-23485-4_54
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