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    SCALABLE COMPLEX EVENT PROCESSING WITH PROBABILISTIC MACHINE LEARNING MODELS TO PREDICT SUBSEQUENT GEOLOCATIONS
    1.
    发明公开
    SCALABLE COMPLEX EVENT PROCESSING WITH PROBABILISTIC MACHINE LEARNING MODELS TO PREDICT SUBSEQUENT GEOLOCATIONS 审中-公开
    基于概率机器学习模型的可扩展复杂事件处理预测后续地理位置

    公开(公告)号：EP3292518A1

    公开(公告)日：2018-03-14

    申请号：EP16790096.8

    申请日：2016-05-05

    申请人： RetailMeNot, Inc.

    发明人： REESE, David, John ,  TABERNER-MILLER, Annette, M. ,  ACHARYA, Sankalp ,  ADAM, Lipphei

    IPC分类号： G06Q10/04 ,  G06F17/30 ,  G06F17/18

    CPC分类号： G06N99/005 ,  G06F17/30241 ,  G06N3/0445 ,  G06N7/005

    摘要： Provided is a process, including: obtaining a set of historical geolocations; segmenting the historical geolocations into a plurality of temporal bins; determining pairwise transition probabilities between a set of geographic places based on the historical geolocations; configuring a compute cluster by assigning subsets of the transition probabilities to computing devices in the compute cluster; receiving a geolocation stream indicative of current geolocations of individuals; selecting a computing device in the compute cluster in response to determining that the computing device contain transition probabilities for the received respective geolocation; selecting transition probabilities applicable to the received respective geolocation from among the subset of transition probabilities assigned to the selected computing device; predicting a subsequent geographic place based on the selected transition probabilities.