Much of the discussion around complex event processing (CEP) focuses on situation-detection applications: watch for something to happen, and alert me when it does. A common but often overlooked use for CEP technology is delivering real-time insight to allow for better and more responsive decision-making. This goes beyond alerting to helping me understand the current state of my business (or my investments or whatever Im trying to manage) and is a form of business activity monitoring (BAM). It may very well include an alerting component to tell me when something happens, but the main goal is to derive insight from all incoming data and deliver it to users in a way that enables better decision-making.
One example of where this is being used is in the investment community, where there is a desire to see aggregated position information related to the holdings in a portfolio along with profit-and-loss calculations - in real time. That last piece is the twist: traditional position-keeping and portfolio management systems are implemented on relational databases. Relational databases are great for real-time (online) transaction processing, but they arent designed for real-time data analysis tasks. Therefore, in a traditional position-keeping system, individual transactions are applied to the database in real time, but the reports that aggregate the transactions to compute net positions and then apply market prices to compute current value and gain/loss are run overnight or at set intervals. A traditional relational database simply cannot keep up with an incoming stream of transactions and another incoming stream of market prices to continuously recompute the values within the portfolio. For CEP engines that are implemented on a relational model, however, this form of real-time continuous computation is exactly what they are designed to do.
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