An algebra for pattern matching, time-aware aggregates and partitions on relational data streams
Sebastian Herbst, Niko Pollner, Johannes Tenschert, Frank Lauterwald, Gregor Endler, Klaus Meyer-Wegener · 2015
Many interesting applications of continuous-query processing are concerned with pattern matching or complex temporal aggregation of events. Real-world queries that rely on these operations are difficult to implement in current stream-processing systems. The reason seems to be a gap between two types of existing query languages: Some languages (e. g. CQL) offer a small set of simple operators that can be combined in order to create complex queries. While these languages provide sound and comprehensible semantics, they lack the expressiveness required for many real-world applications. Other approaches (e. g. Aurora) provide powerful operators but lack semantic strictness, which is required for reasoning about query results. Such reasoning is a prerequisite for safe query optimization.