Learning temporal causal structures between time series is one of the key tools for analyzing time series data. In many real-world applications, we are confronted with Irregular T...
Recent research has made significant advances in automatically constructing knowledge bases by extracting relational facts (e.g., Bill Clinton-presidentOf-US) from large text cor...
Partha Pratim Talukdar, Derry Tanti Wijaya, Tom Mi...
Temporal causal modeling can be used to recover the causal structure among a group of relevant time series variables. Several methods have been developed to explicitly construct te...
This paper reports on a mechanism to identify temporal spatial trends in social networks. The trends of interest are defined in terms of the occurrence frequency of time stamped p...
Puteri N. E. Nohuddin, Rob Christley, Frans Coenen...
Although many spatio-temporal conceptual models has been proposed in the last years, users must express their queries on the underlying physical data structures. In the context of ...