  The tremendous growth of positioning technologies and GPS enabled devices has
produced huge volumes of tracking data during the recent years. This source of
information constitutes a rich input for data analytics processes, either
offline (e.g. cluster analysis, hot motion discovery) or online (e.g.
short-term forecasting of forthcoming positions). This paper focuses on
predictive analytics for moving objects (could be pedestrians, cars, vessels,
planes, animals, etc.) and surveys the state-of-the-art in the context of
future location and trajectory prediction. We provide an extensive review of
over 50 works, also proposing a novel taxonomy of predictive algorithms over
moving objects. We also list the properties of several real datasets used in
the past for validation purposes of those works and, motivated by this, we
discuss challenges that arise in the transition from conventional to Big Data
applications.
  CCS Concepts: Information systems > Spatial-temporal systems; Information
systems > Data analytics; Information systems > Data mining; Computing
methodologies > Machine learning Additional Key Words and Phrases: mobility
data, moving object trajectories, trajectory prediction, future location
prediction.
