Personalized diagnosis and therapy requires monitoring patient activity using various body
sensors. Sensor data generated during personalized exercises or tasks may be too specific …

Abstract Mining frequent Time Intervals-Related Patterns (TIRPs) from series of symbolic
time intervals offers a comprehensive framework for heterogeneous, multivariate temporal …

In this paper, we study the problem of classification of sequences of temporal intervals. Our
main contribution is a novel framework, which we call SMILE, for extracting relevant features …

Sequences of event intervals appear in several application domains including sign
language, sensor networks, medicine, human motion databases, and linguistics. Such …

Objectives A common prerequisite for tasks such as classification, prediction, clustering and
retrieval of longitudinal medical records is a clinically meaningful similarity measure that …

The study of human activity in space and time is an inherent part of human geography. In
order to perform such studies, data on the time use of individuals, in terms of sequence and …

Almost all activities observed in nowadays applications are correlated with a timing
sequence. Users are mainly looking for interesting sequences out of such data. Sequential …

In this paper, we study the problem of classification of sequences of temporal intervals. Our
main contribution is the STIFE framework for extracting relevant features from interval …

In several application domains, including sign language, sensor networks, and medicine,
events are not necessarily instantaneous but they may have a time duration. Such events …

Abstract Symbolic Time Intervals (STIs) describe events having a non-zero time duration,
which occur in a wide range of application domains. In this paper, we target the challenge of …