Semantic Query Optimisation in Complex Event Processing Technologies

The project QONCEPT develops and evaluates methods for semantic query optimisation in Complex Event Processing.

         In databases, the idea of using application-specific knowledge about (possible) data to make query evaluation more efficient, is called semantic query optimisation.

         Querying events is fundamentally different from traditional querying in databases or querying of Web data, since event queries are standing queries evaluated permanently over time against changing event data received as an incoming stream. Algorithmic methods for making sense of events by deriving higher-level knowledge, or complex events, from lower-level events in a timely fashion and permanently is comprised by the notion Complex Event Processing (CEP). Examples are the recognition of trends in markets, of situations that might become difficult or dangerous in transport systems, or of risks and failures in industrial or manufacturing processes.

         In the project QONCEPT, properties of an incoming event stream will be used for optimising the evaluation of complex event queries against this event stream. Properties with regard to contents of events as well as structural, causal and temporal dependencies between events will be considered. Such properties are present in most of the CEP applications. Similar properties have proven to be very useful in optimising the evaluation of standard queries. Their use for query optimisation in CEP is therefore promising.

1. Event Stream Constraint Language (ESCL)
2. Instantiating Hierarchical Timed Automata (IHTA)
3. Derivation of ESCL constraints from IHTA
4. Propagation of ESCL constraints with respect to CEP queries
5. Derivation of ESCL constraints from CEP queries independently from other constraints
6. Subsumption of CEP conjunctive queries
7. Semantic rewriting of CEP queries with respect to ESCL constraints
8. Choice of one of the alternatively semantically optimized queries to evaluate instread of each initial query using cost models
9. Implementation, experimental evaluation, visual editor