{"type":"ScholarlyArticle","title":"Learning Complex Event Descriptions by Abstraction","authors":[{"name":"Ugo Galassi","slug":"ugo-galassi","given":"Ugo","family":"Galassi","affiliation":""},{"name":"Attilio Giordana","slug":"attilio-giordana","given":"Attilio","family":"Giordana","affiliation":""},{"name":"Lorenza Saitta","slug":"lorenza-saitta","given":"Lorenza","family":"Saitta","affiliation":""},{"name":"Marco Botta","slug":"marco-botta","given":"Marco","family":"Botta","affiliation":""}],"abstract":"The presence of long gaps dramatically increases the difficulty of detecting and characterizing complex events hidden in long sequences. In order to cope with this problem, a learning algorithm based on an abstraction mechanism is proposed: it can infer a Hierarchical Hidden Markov Model, from a learning set of sequences. The induction algorithm proceeds bottom-up, progressively coarsening the sequence granularity, and letting correlations between subsequences, separated by long gaps, naturally emerge. As a case study, the method is evaluated on an application of user profiling. The results show that the proposed algorithm is suitable for developing real applications in network security and monitoring.","doi":null,"url":"https://ijcai.org/proceedings/2005/268","pdf":"https://ijcai.org/proceedings/2005/0268.pdf","markdown":"https://ijcai.org/proceedings/2005/268.md","bibtex":"https://ijcai.org/proceedings/2005/bibtex/268","volume":{"slug":"2005","title":"Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2005","url":"https://ijcai.org/proceedings/2005/","year":2005,"location":"Edinburgh, Scotland, UK","dates":"","editors":"","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":268,"pages":{"first":1600,"last":1600},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}