首页>
外国专利>
SCALABLE COMPLEX EVENT PROCESSING WITH PROBABILISTIC MACHINE LEARNING MODELS TO PREDICT SUBSEQUENT GEOLOCATIONS
SCALABLE COMPLEX EVENT PROCESSING WITH PROBABILISTIC MACHINE LEARNING MODELS TO PREDICT SUBSEQUENT GEOLOCATIONS
展开▼
机译:利用概率机器学习模型可预测的复杂事件的可扩展复杂事件处理
展开▼
页面导航
摘要
著录项
相似文献
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.
展开▼