Scaling spatial big data in a location-based social network

The widespread of the World Wide Web has resulted in a high volume of volunteered generated information using different formats including text, photography and video. The technological advances of recent years enabled the emergence and the popularization of various mobile devices equipped with GPS and connectivity to the Internet. This scenario contributed to the advent of several location-based applications and aroused the interest of many users in the geographical context of the information. An example of such applications are the Location-Based Social Networks (LBSN), in which the users interact with information classified by geographic region, as in the context of Smart Cities, in which citizens can interact pinning their criticisms, opinions and comments on various topics related to their city or neighborhood. The LBSNs have increasingly attracted the interest of the population and have consequently registered an increase in both the number of users interacting and the volume of shared information. This popularity brings up concerns about scalability, since it is essential to provide an environment that maintains the users active and motivated for contributing. Thus, the LBSNs must ensure acceptable response times, especially in spatial queries performed by their users, otherwise such applications may collapse due to the abandonment of their faithful users. Among several proposals of LBSNs in the community, it is still difficult to find out approaches concerned in scalability. In this context, this paper proposes an approach based on Big Data technologies to provide scalability in LBSNs and thus handle large volumes of spatial data. Our approach exploits NoSQL databases, the Map/Reduce technique and the development of extensions for indexing and querying Spatial Big Data.