作者: Guy Jacobson , Greg B. Kinne , Arnold Lent , Colin Goodall
DOI:
关键词: Table (database) 、 Multi domain 、 Data mining 、 Anomaly (natural sciences) 、 Raw data 、 Health risk 、 Animal data 、 Algorithmic probability 、 Subject-matter expert 、 Geography
摘要: Disclosed herein is a multi-domain anomaly pattern definition and detection module. The module receives raw data from different kinds of anomalies variety algorithms generates scores associated with the data. If one or more exceed threshold, then algorithm gathers further information which may include counts listings detailed for geographic region such as emergency department lab related to particular health concern respiratory syndrome. Summaries are provided identify numbers events according utilizing probability algorithms. Other databases animal collected under Department Agriculture also be utilized. presented in familiar form map table that subject matter expert determine whether investigate an potential risk, example, risk.