The excessive amount of data that online users produce through social media platforms provides valuable insights about users and communities at scale. Existing techniques have not fully exploited such data to help practitioners perform a deep analysis of large online communities. Lack of scalability hinders analyzing communities of large sizes and requires tremendous system resources and unacceptable runtime. This paper introduces a new analytical query that reveals the top-k posts of interest of a given user community over a period of time and in a certain location. We propose a novel indexing framework that captures the interactions of community users to provide a low query latency. Moreover, we propose efficient query algorithms that utilize the index content to prune the search space. The extensive experimental evaluation on real data has shown the superiority of our techniques and their scalability to support large online communities.

This paper demonstrates DLEEL; a research system that supports scalable spatial queries with multiple predicates on user-generated data streams, such as social media streams. Supported queries include spatial-social queries and spatial-keyword queries, which are popular in different applications but have never been addressed in the challenging environment of streaming data, where data arrives with excessively high rates. DLEEL distinguishes itself with three novel contributions: (1) Indexing spatial-social data in for personalized real-time search: DLEEL is the first to address personalized queries on streaming spatial- social data through novel low-overhead indexing that scales for large amounts of data and users. The novel indexing has a hybrid storage architecture that trades off indexing overhead, memory consumption, and query latency. (2) Indexing spatial-keyword data for real-time search: DLEEL is the first to enrich existing spatial-keyword indexes with novel streaming data components. The new components reveal performance losses and gains from a system perspective, trading off the system overhead with flexibility to support a variety of queries. (3) Scalable query processing: DLEEL exploits the indexes content to smartly prune the search space on multiple dimensions and support efficient query latency for its different queries on excessive number of data records. DLEEL is demonstrated using a stream of 5 billions real tweets collected from Twitter APIs and real query locations obtained from a popular web search engine. DLEEL has shown superior performance with serving incoming queries with an average latency of few milliseconds while digesting hundreds of thousands of data records every second.

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 real-time 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.

This paper provides an extensive experimental evaluation for different spatial-keyword index structures on streaming data. We extend existing snapshot spatial-keyword queries with the temporal dimension to effectively serve streaming data applications. Then, the major index structures are equipped with efficient query processing techniques and evaluated to process the extended queries. The evaluation is oriented towards a system building perspective to provide system builders with insights on supporting scalable spatial-keyword queries on fast data streams, e.g., social media streams and news streams. In particular, we have taken existing spatial-keyword index structures apart into four major building blocks that are commonly supported at a system-level. Ten different index structures are then composed as combinations of these four building blocks. The ten indexes are wholly residents in main-memory, and they are evaluated on real datasets and query locations. The index performance is measured in terms of data digestion rate in real time, main-memory footprint, and query latency. The results show the relative performance gains of both basic and hybrid index structures with abundant insights from a system point of view.