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This paper presents a novel, generic, and automatic method for data-drivensite selection. Site selection is one of the most crucial and importantdecisions made by any company. Such a decision depends on various factors ofsites, including socio-economic, geographical, ecological, as well as specificrequirements of companies. The existing approaches for site selection (commonlyused by economists) are manual, subjective, and not scalable, especially to BigData. The presented method for site selection is robust, efficient, scalable,and is capable of handling challenges emerging in Big Data. To assess theeffectiveness of the presented method, it is evaluated on real data (collectedfrom Federal Statistical Office of Germany) of around 200 influencing factorswhich are considered by economists for site selection of Supermarkets inGermany (Lidl, EDEKA, and NP). Evaluation results show that there is a bigoverlap (86.4 \%) between the sites of existing supermarkets and the sitesrecommended by the presented method. In addition, the method also recommendsmany sites (328) for supermarket where a store should be opened.

A Novel Approach for Data-Driven Automatic Site Recommendation and Selection