  In Big data era, information integration often requires abundant data
extracted from massive data sources. Due to a large number of data sources,
data source selection plays a crucial role in information integration, since it
is costly and even impossible to access all data sources. Data Source selection
should consider both efficiency and effectiveness issues. For efficiency, the
approach should achieve high performance and be scalability to fit large data
source amount. From effectiveness aspect, data quality and overlapping of
sources are to be considered, since data quality varies much from data sources,
with significant differences in the accuracy and coverage of the data provided,
and the overlapping of sources can even lower the quality of data integrated
from selected data sources.
  In this paper, we study source selection problem in \textit{Big Data Era} and
propose methods which can scale to datasets with up to millions of data sources
and guarantee the quality of results. Motivated by this, we propose a new
object function taking the expected number of true values a source can provide
as a criteria to evaluate the contribution of a data source. Based on our
proposed index we present a scalable algorithm and two pruning strategies to
improve the efficiency without sacrificing precision. Experimental results on
both real world and synthetic data sets show that our methods can select
sources providing a large proportion of true values efficiently and can scale
to massive data sources.
