Temporal Geo-Social Personalized Search Over Streaming Data

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 geo-social 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.