Representing Temporal Attributes for Schema Matching
Aug 13, 20201 views
Temporal data are prevalent, where one or several time attributes,present. It is challenging to identify the temporal attributes from,heterogeneous sources. The reason is that the same attribute could,contain distinct values in different time spans, whereas different,attributes may have highly similar timestamps and alike values.,Existing studies on schema matching seldom explore the temporal,information for matching attributes. In this paper, we argue to order the values in an attribute,A,by some time attribute,T,as a time,series. To learn deep temporal features in the attribute pair,(,T,,,A,),,,we devise an auto-encoder to embed the transitions of values in the,time series into a vector. The temporal attribute matching (TAM) is,thus to evaluate matching distance of two temporal attribute pairs,by comparing their transition vectors. We show that computing,the optimal matching distance is,np,-hard, and present an approximation algorithm. Experiments on real datasets demonstrate the,superiority of our proposal in matching temporal attributes compared to the generic schema matching approaches.