Abstract
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.