Abstract
Events are occurrences in specific locations, time, and semantics that
nontrivially impact either our society or the nature, such as
earthquakes, civil unrest, system failures, pandemics, and crimes. It is
highly desirable to be able to anticipate the occurrence of such events
in advance in order to reduce the potential social upheaval and damage
caused. Event prediction, which has traditionally been prohibitively
challenging, is now becoming a viable option in the big data era and is
thus experiencing rapid growth, also thanks to advances in high
performance computers and new Artificial Intelligence techniques. There
is a large amount of existing work that focuses on addressing the
challenges involved, including heterogeneous multi-faceted outputs,
complex (e.g., spatial, temporal, and semantic) dependencies, and
streaming data feeds. Due to the strong interdisciplinary nature of
event prediction problems, most existing event prediction methods were
initially designed to deal with specific application domains, though the
techniques and evaluation procedures utilized are usually generalizable
across different domains. However, it is imperative yet difficult to
cross-reference the techniques across different domains, given the
absence of a comprehensive literature survey for event prediction. This
paper aims to provide a systematic and comprehensive survey of the
technologies, applications, and evaluations of event prediction in the
big data era. First, systematic categorization and summary of existing
techniques are presented, which facilitate domain experts’ searches for
suitable techniques and help model developers consolidate their research
at the frontiers. Then, comprehensive categorization and summary of
major application domains are provided to introduce wider applications
to model developers to help them expand the impacts of their research.
Evaluation metrics and procedures are summarized and standardized to
unify the understanding of model performance among stakeholders, model
developers, and domain experts in various application domains. Finally,
open problems and future directions for this promising and important
domain are elucidated and discussed.