Reducing complex problems sets to simple problem sets is an interesting, and sometimes valid, approach to complex event processing. Transformations can be useful, especially when well defined.
For example, CEP was envisioned as a new technology to debug relatively large distributed systems, discover hidden causal relationships in seemingly disconnected event space. This “discovery” requires backwards chaining with uncertainty, for example. Most of the current so-called “CEP software” (on the market today (including Marc Adler’s SQL-based examples) do not perform backwards chaining (with uncertainty). This is also true from other so-called CEP products, like most forward chaining RETE engines – for example, see this post.
Marc Adler says he is, “hunting for advice from people who might have implemented event clouds in Coral8, Streambase, and Aleri, all three which are based on SQL.”
Current steaming SQL engines cannot model true event clouds without reducing the cloud to causal-ordered sets of linear streaming data. These software tools are stream processing engines that process events in a time window of continuous streaming data. These products are not, in reality, CEP engines – calling them “CEP engines” is marketing-speak, not technology-speak!
Reducing complex models to simple ones is a valid technique for problem solving. Likewise, eliminating uncertainty and assuming causality is a way to reduce complexity.
CEP was envisioned to discover causal relationships in complex, uncertain, “cloudy data” and the current state-of-the-art of software from the streaming SQL vendors do not have this capability, unless you reduce all event models to ordered sets of streaming data (reduce POSETS to TOSETS).
Reductionism can be a valid technique, of course. We can eliminate uncertainty, force strict determinism, demand apriori system rules and perform all sorts of tricks to permit us to reduce complex problems to simple ones.
However this also results in reducing CEP (complex event processing) to SEP (simple event processing).