Research activity in 2001
In 2001, the group continued to be active on several topics in collaboration with the group of Machine Learning at the Università del Piemonte Orientale at Alessandria: for what concerns learning techniques in First Order Logic, algorithms previously developed in the systems NTR, FONN and G-Net have been further extended and integrated in a unique framework. Moreover, a boosting technique has been devised and integrated in the same learning framework.
A new research interest started around the analysis of event sequences that present interesting problems for machine learning techniques. Discovering complex events made of simple ones in sequences of events is a quite hard task that is useful in several application domains, such as log analysis, network traffic analysis, and bioinformatics.
The research activity of the group continues also around the extraction of statistical dependencies among data. The new paradigm of data dependence, that allows to find the actual significant dependencies among data, is studied in depth and finds some applications not only for structured data and databases but also for WEB data and texts (see references section).
Furthermore, the concept of data dependence has been studied from the viewpoint of the Information Theory, and more in particular, a renewed definition of the mutual information is investigated. Finally, the concept of support of patterns (such as the itemsets) that in Data Mining is based on simple statistical measures is currently analysed in order to propose a well-founded, theoretical definition based on the Bayes' Theorem.
A new branch of research centered on the analysis of the role that databases play in the knowledge discovery process has been started in 2000 and continued in 2001. This new area of research named "inductive databases" aims at studying the potentialities of query languages in the iterative process of knowledge discovery. Inductive databases are studied also from the viewpoint of the evaluation and execution of the queries.
M. Botta.. Programming
Resampling vs Reweighting in Boosting a Relational Weak Learner. LNAI, Vol. 2175, pages 70-80,
R. Esposito, L. Saitta. Boosting as a Monte Carlo Algorithm. LNAI, Vol. 2175, pages 11-19