---
title: "Learning Complex Event Descriptions by Abstraction"
authors:
  - "Ugo Galassi"
  - "Attilio Giordana"
  - "Lorenza Saitta"
  - "Marco Botta"
doi: ""
url: "https://ijcai.org/proceedings/2005/268"
pdf: "https://ijcai.org/proceedings/2005/0268.pdf"
venue: "Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence"
year: 2005
pages: "1600-1600"
track: ""
keywords:
---

# Learning Complex Event Descriptions by Abstraction

Ugo Galassi, Attilio Giordana, Lorenza Saitta, Marco Botta

*Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence*, pages 1600-1600.

## 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.