科研之友
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摘要
Location-based services (LBSs) are becoming an increasingly important component in our social and business life. All existing LBS providers support the nearest place searching via a single point of interest (POI) query. That is, in one query, a user is allowed to search for only one type of service. However, in real life, people usually need to search multiple different types of services and hope that their locations are as close as possible for convenience. For example, one user would like to search for a restaurant with a KTV nearby. To support this application scenario, we propose a novel LBS termed "N-in-One," which is the first scheme to extend the function of single-POI LBS to multiple-POI LBS such that a single query can be employed to request multiple POIs that are geographically close. Providing "N-in-One" is challenging because: 1) serving a "N-in-One" query is not equivalent to serving N queries independently due to the distance correlation among the N POIs; and 2) the cask effect is getting worse in the service area mode of "N-in-one" as most of the returned results may be rendered useless when some hot POIs are blocked. To overcome these challenges, we propose several algorithms using computational geometry techniques to identify the best K POIs that are geographically close and the service area (denoted by a given-sized rectangle) that can cover as many the best Q clusters as possible while reducing the cask effect in the service area mode. Extensive simulations based on both synthetic and real world data demonstrate the effectiveness of the proposed algorithms.
