Continuously Updating Query Results over Real-Time Linked Data
Existing solutions to query dynamic Linked Data sources extend the SPARQL language, and require continuous server processing for each query. Traditional SPARQL endpoints accept highly expressive queries, contributing to high server cost. Extending these endpoints for time-sensitive queries increases the server cost even further. To make continuous querying over real-time Linked Data more affordable, we extend the low-cost Triple Pattern Fragments (TPF) interface with support for time-sensitive queries. In this paper, we discuss a framework on top of TPF that allows clients to execute SPARQL queries with continuously updating results. Our experiments indicate that this extension significantly lowers the server complexity. The trade-off is an increase in the execution time per query. We prove that by moving the complexity of continuously evaluating real-time queries over Linked Data to the clients and thus increasing the bandwidth usage, the cost of server-side interfaces is significantly reduced. Our results show that this solution makes real-time querying more scalable in terms of cpu usage for a large amount of concurrent clients when compared to the alternatives.