An Implementation of Area Skyline Query to Select Facilities Location Based on User's Preferred Surrounding Facilities

Selecting a good location to build facilities such as stockiest to support e-business becoming important parts of strategic decision as a profound effect on supply chain excellence in the market competition and operation. Previous location selection methods have their predefined candidate points. However, in a real-world situation candidate points for selection is not always available. This research presents an implementation of Grid Based Area Skyline Query (GASKY), a big data-based approach to select a group of interesting areas by using preferred surrounding facilities without considering any candidate point. In order to provide the efficacy of this approach, we equipped a dataset based on a real case study at a sub-district around industrial estate. For initial steps, four different numbers of grids were tested in each dataset, with size of 144 (derived from 12 × 12 grid), 256 (16 × 16), 400 (20 × 20), and 625 (25 × 25) grids, with restaurant and housing complex acts as the preferred desirable surrounding facilities. In parallel, similar competitor's food supplier, meat poultry shop, garbage dump, and chemical plants act as the undesirable surrounding facilities. This approach worked well to provide solution in the target area in real-world problems. The result shows that the recommended interesting areas reflect priority location without any pre-defined list of candidates.