Patent ID: 8892484
Filing Date: 2014-11-18
Classification: G06N

Abstract:
1. A method for predicting significant future events based on previous events, comprising: receiving plural messages representing events, each message including an event type attribute, a time attribute, a population attribute, and a semantic attribute; mapping the attributes of the messages to respective feature dimensions to define a multidimensional histogram; determining co-occurrence of at least two event types based on queries of the multidimensional histogram; clustering correlated event types based on the corresponding event type attributes using a multiple assignment hierarchal clustering algorithm; estimating a probability density function corresponding to multiple feature dimensions for each cluster of related event types; detecting anomalous event types from the messages by comparing feature dimensions of incoming messages to the probability density functions of the cluster corresponding to the event type of the incoming messages; identifying highly anomalous event types in clusters based on the probability density functions; aggregating similar anomalous event type clusters to create an anomaly template of multiple pairs of event types; establishing a temporal sequence of each event type in the anomaly template; estimating causal relationships between each pair of event types in the anomaly template; constructing a Bayesian belief network of the pairs of event types; predicting a significant event by applying the Bayesian belief network to an incoming message; and applying event types from the Bayesian belief network onto a timeline to establish a sequential set of events related to the significant event.