1 research outputs found
Event Prediction in the Big Data Era: A Systematic Survey
Events are occurrences in specific locations, time, and semantics that
nontrivially impact either our society or the nature, such as civil unrest,
system failures, and epidemics. 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. There is a large amount of existing
work that focuses on addressing the challenges involved, including
heterogeneous multi-faceted outputs, complex dependencies, and streaming data
feeds. 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. 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