Applying proximity rank join model into location-based services

Joining objects from different web sources and returning top-k combinations is a research topic with much attention. Such techniques could be used in location based scenarios, which for one example, help plan a wonderful night by finding a good combination of hotel, restaurant and theater. Challenge to such techniques is that in a good combination, each individual object should be good enough and, of equal or even greater importance, subject to some given criteria, such as within a given range and close to each other. Proximity rank join method is one way to settle this problem. It takes advantage of sorted access of inputs, does not rely on specialize data structures to determine spatial closeness, and has efficient pull and bound strategies to avoid reading too much inputs before finding the top-k answers. In this paper, we propose a detailed scheme using the proximity rank join model that is optimized for location based purposes. It will be shown that our scheme indeed reflects user's desire better and outperform the naïve Euclidean distance scheme.