Schema Evolution publication categorizer

Data Stream Management Systems (DSMSs) do not statically respond to issued queries — rather, they continuously produce result streams to standing queries, and often operate in a context where any interruption can lead to data loss. Support for schema evolution in continuous query processing is currently unaddressed. In this work we address evolution in DSMSs by proposing semantics for three evolution primitives: Add Attribute and Drop Attribute (schema evolution), and Alter Data (data evolution). We characterize how a subset of commonly used query operators in a DSMS act on and propagate these primitives.