Modern, inherently dynamic systems are usually characterized by a network structure which
is subject to discrete changes over time. Given a static underlying graph, a temporal graph …

Qualitative spatial/temporal reasoning (QSR/QTR) plays a key role in research on human
cognition, eg, as it relates to navigation, as well as in work on robotics and artificial …

As the analysis of big datasets in sports performance becomes a more entrenched part of
the sporting landscape, so the value of sport scientists and analysts with formal training in …

Cycles are fundamental to human health and behavior. Examples include mood cycles,
circadian rhythms, and the menstrual cycle. However, modeling cycles in time series data is …

Abstract Qualitative Spatial & Temporal Reasoning (QSTR) is a major field of study in
Symbolic AI that deals with the representation and reasoning of spatiotemporal information …

We study here the problem of exploring a temporal graph when the underlying graph is a
star. The aim of the exploration problem in a temporal star is finding a temporal walk which …

We investigated the impact of sleep and training load of Division-1 women's basketball
players on their game performance and injury prediction using machine learning algorithms …

Sport analytics allows sport teams and organizations to improve performance and
associated business decisions. There is an increasing demand for sport analytics, in part …

We introduce and study a notion of robustness in Qualitative Constraint Networks (QCNs),
which are typically used to represent and reason about abstract spatial and temporal …

In recent years, sport analytics evolved in the massive collection of data, especially from
Global Positioning System (GPS) sensors installed in sport facilities or worn by the athletes …