  This paper presents a novel, generic, and automatic method for data-driven
site selection. Site selection is one of the most crucial and important
decisions made by any company. Such a decision depends on various factors of
sites, including socio-economic, geographical, ecological, as well as specific
requirements of companies. The existing approaches for site selection (commonly
used by economists) are manual, subjective, and not scalable, especially to Big
Data. The presented method for site selection is robust, efficient, scalable,
and is capable of handling challenges emerging in Big Data. To assess the
effectiveness of the presented method, it is evaluated on real data (collected
from Federal Statistical Office of Germany) of around 200 influencing factors
which are considered by economists for site selection of Supermarkets in
Germany (Lidl, EDEKA, and NP). Evaluation results show that there is a big
overlap (86.4 \%) between the sites of existing supermarkets and the sites
recommended by the presented method. In addition, the method also recommends
many sites (328) for supermarket where a store should be opened.
