The Hierarchical Hidden Markov Model (HHMM) is a well formalized tool suitable to model complex patterns in long temporal or spatial sequences. Even if effective algorithms are ava...
The feature interaction problem is prominent in telephone service development. Through a number of case studies, we have discovered that no single semantic framework is suitable f...
J. Paul Gibson, Geoff Hamilton, Dominique Mé...
A new publish/subscribe capability is presented: the ability to predict the likelihood that a subscription will be matched at some point in the future. Composite subscriptions con...
The problem of locating motifs in real-valued, multivariate time series data involves the discovery of sets of recurring patterns embedded in the time series. Each set is composed...
David Minnen, Charles Lee Isbell Jr., Irfan A. Ess...
This paper describes a new language resource of events and semantic roles that characterize real-world situations. Narrative schemas contain sets of related events (edit and publi...