Temporal geo-social personalized search over streaming data

Abstract

The unprecedented rise of social media platforms, combined with location-aware technologies, has led to continuously producing a significant amount of geo-social data that flows as a user-generated data stream. This data has been exploited in several important use cases in various application domains. This paper supports geosocial personalized queries in streaming data environments that have not been addressed in the existing literature. We define two temporal geo-social queries that provide users with real-time personalized answers based on their social graph. Then, we propose an indexing framework that provides lightweight and effective realtime indexing to digest geo-social data in real time. The framework distinguishes highly-dynamic data from relatively-stable data and uses appropriate data structures and storage tier for each. Based on this framework, we propose a novel geo-social index and adopt two baseline indexes to support the addressed queries. The query processor then employs different types of pruning to efficiently access the index content and provide real-time query response. The extensive experimental evaluation based on real datasets has shown the superiority of our proposed techniques to index real-time data and provide low-latency queries compared to existing competitors.