Yilmaz, EmreElbaşı, SanemFerhatosmanoğlu, Hakan2018-04-122018-04-122017-08http://hdl.handle.net/11693/37557Date of Conference: 13-17 August, 2017Conference name: KDD '17 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data MiningDeriving meaningful insights from location data helps businesses make better decisions. One critical decision made by a business is choosing a location for its new facility. Optimal location queries ask for a location to build a new facility that optimizes an objective function. Most of the existing works on optimal location queries propose solutions to return best location when the set of existing facilities and the set of customers are given. However, most businesses do not know the locations of their customers. In this paper, we introduce a new problem setting for optimal location queries by removing the assumption that the customer locations are known. We propose an optimal location predictor which accepts partial information about customer locations and returns a location for the new facility. The predictor generates synthetic customer locations by using given partial information and it runs optimal location queries with generated location data. Experiments with real data show that the predictor can find the optimal location when sufficient information is provided. © 2017 Copyright held by the owner/author(s).EnglishData generationLocation analyticsOptimal location queriesPredictionUncertaintyData miningForecastingSalesObjective functionsOptimal facility locationOptimal locationsOptimal-location queryPartial informationSet of customersUncertaintyLocationConference Paper10.1145/3097983.3098198