Description

With the ever-growing popularity of mobile devices (e.g., smart phones), location-based service (LBS) systems (e.g., Google Maps for Mobile) have been widely deployed and accepted by mobile users. The k-nearest neighbor (kNN) search on road networks is a fundamental problem in LBS. Given a query location and a set of static objects (e.g., restaurant) on the road network, the kNN search problem finds k nearest objects to the query location. Alone with the popular usage of LBS, the past few years have witnessed a massive boom in location-based social networking services like Foursquare, Yelp, Loopt, Geomium and Facebook Places. In all these services, social network users are often associated with some locations (e.g., home/office addresses and visiting places). Such location information, bridging the gap between the physical world and the virtual world of social networks, presents new opportunities for the kNN search on road networks. Our paper spotlight on an influential query in methodical reproduction data analysis: the Spatial Distance Histogram (SDH). Propose a greatly efficient comparative algorithm to count SDH over ensuing time periods For this in the Enhancement work we will enhance the work by implementing the k-nearest neighbor search by using RSkNN called kNN search on road networks by incorporating social influence One critical challenge of the problem is to speed up the computation of the social influence over large road and social networks. To address this challenge, we propose three efficient index-based search algorithms, i.e., road network-based (RN-based), social network-based (SN-based) and hybrid indexing algorithms. We propose highly efficient approximate algorithm to compute SDH over consecutive time periods with provable error bounds. The key idea of our algorithm is to derive statistical distribution of distances from the spatial and temporal characteristics of particles.