  As the fundamental phrase of collecting and analyzing data, data integration
is used in many applications, such as data cleaning, bioinformatics and pattern
recognition. In big data era, one of the major problems of data integration is
to obtain the global schema of data sources since the global schema could be
hardly derived from massive data sources directly. In this paper, we attempt to
solve such schema integration problem. For different scenarios, we develop
batch and incremental schema integration algorithms. We consider the
representation difference of attribute names in various data sources and
propose ED Join and Semantic Join algorithms to integrate attributes with
different representations. Extensive experimental results demonstrate that the
proposed algorithms could integrate schemas efficiently and effectively.
