[文章摘要]Semantic Enrichment and Analysis of Movement Data:Probably it is just Starting!
2016-01-18 13:42
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文章:Semantic Enrichment and Analysis of Movement Data:Probably it is just Starting!
作者:Fileto R, Bogorny V, May C, et al.
来源:SIGSPATIAL Special, 2015, 7(1): 11-18.
本文综述了语义轨迹,并简单介绍了正在进行的工作。
轨迹的语义包含很多维度(见CONSTAnT模型):
Moving object,Space,Time,Goal,Transportation Means,Environment Conditions,Activity等。
得到语义轨迹后,可以进行Semantic Analysis和Semantic Mining。
利用PoI数据来对geo-tweets进行标注,考了两个方面:空间邻近性和文本相似性。
如果仅考虑空间邻近性,则如下图,最近的POI却不是需要访问的POI,这是由于位置含有误差,有些地区POI密度很高,且POI甚至有可能重叠。
本方法对参数非常敏感。
作者:Fileto R, Bogorny V, May C, et al.
来源:SIGSPATIAL Special, 2015, 7(1): 11-18.
本文综述了语义轨迹,并简单介绍了正在进行的工作。
轨迹的语义包含很多维度(见CONSTAnT模型):
Moving object,Space,Time,Goal,Transportation Means,Environment Conditions,Activity等。
得到语义轨迹后,可以进行Semantic Analysis和Semantic Mining。
利用PoI数据来对geo-tweets进行标注,考了两个方面:空间邻近性和文本相似性。
如果仅考虑空间邻近性,则如下图,最近的POI却不是需要访问的POI,这是由于位置含有误差,有些地区POI密度很高,且POI甚至有可能重叠。
本方法对参数非常敏感。
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