Patent ID: 8887286

Claim:
A data-driven method of continuous anomaly detection based on behavioral modeling and heterogeneous information analysis that does not require definition of a set of rules or definition of anomalous patterns, the method comprising: collecting heterogeneous, structured and unstructured, text-bearing and non-text-bearing sociological data that includes data on human behavior; processing and categorizing a plurality of events in quasi-real-time; clustering the plurality of events from the sociological data in quasi-real-time; building predictive models of at least one of individual behavior and collective behavior in quasi-real-time for behavior and information analysis; analyzing behavior and information based on a multidimensional, normalcy-based behavioral model; detecting behavioral anomalies in the collected sociological data; displaying an animated and interactive visualization of the multidimensional, normalcy-based behavioral model; and displaying an animated and interactive visualization of the detected behavior-based and time-based anomalies, wherein different types of anomalies are detected based on the results of data analysis and individual and collective behavior modeling, wherein a monitoring or anomaly detection system provides a source of anomalies, wherein baseline patterns are computed with respect to different referentials and used as a source of anomalies corresponding to deviations from the baseline patterns, and wherein rankings of individuals against behavioral traits are a source of anomalies corresponding to abnormal behavior.