Semantic Load Shedding over Real-Time Data Streams

When streams rates exceed the system capacity, a Data Stream Management System (DSMS) becomes overloaded and fails to satisfy all kinds of requirements, such as tuple latency and result precision. Especially, in a time-critical environment, queries should be completed not just timely but within certain deadlines. Semantic load shedding is an effective approach to alleviate workloads. In order to improve the efficiency of load shedding over real-time data streams, we present a semantic and flexible load shedding algorithm based on priority table (SLS-PT) which considers about execution costs and tuples' values together when deciding which tuples are dropped.