Survey of Efficient Filtering Algorithms for Location-Aware Subscribe with R-Tree Model

Abstract: In this paper survey LBS systems employ a pull model or user-initiated model, where a user issues a query to a server which responds with location aware answers. To provide users with instant replies, a push model or server-initiated model is becoming an inevitable computing model in next-generation location-based services. In the push model, subscribers register spatio-textual subscription to capture their interests, and publishers post spatio textual messages. These calls for a high-performance location-aware publish/subscribe system to deliver messages to relevant subscribers. This computing model bring new user experiences to mobile users, and can help users recover information without explicitly issue a query. The publish/subscribe system should support tens of millions of subscribers and deliver messages to relevant subscribers in milliseconds. while messages and subscriptions contain both location information and textual description, it is rather costly to deliver messages to relevant subscribers. These calls for an efficient filtering technique to support location-aware publish/subscribe services. Moreover, a prediction strategy is proposed to predict the subsequent mobile behaviors. Mean while, a time segmentation approach is presented to find segmenting time intervals where similar mobile characteristics exist. To the best knowledge, this is the first work on mining and prediction of mobile behaviors with considerations of user relations and temporal property simultaneously. Through survey under various the proposed methods are shown to deliver excellent performance.

Keywords: LBS, MBR Filter, Pull Model, Push Model.