Complex event processing (CEP) is a helpful technique for real-time informational queries. Also known as a complex event event stream, you can query data before it’s stored in a database. In some instances, these data stream queries aren’t stored at all. CEP technology is ideal for varied use cases such as tracking real-time events, verifying event data, and pinpointing an event source.
While complex events may seem, well, complex, they don’t necessarily have to be. With the right CEP application or CEP system, you can get greater insight into the event context and leverage multiple data sets. This is what you need to know.
So, what is complex event processing (CEP)? CEP systems allow you to aggregate a combination of events in a similar manner as event stream processing, also referred to as stream processing. You can filter out irrelevant data and send important data to the event cloud or another database. These days, a vast majority of businesses deal with massive amounts of information. Many of them don’t know how to properly leverage CEP to track business events and parse important data. Regardless of the event source, CEP provides actionable insights into real-time performance.
Some use cases include stock trades, algorithmic trading, and the ability to correlate historical data against incoming events. Of course, how a business uses big data will depend on its chosen CEP engine. A stream of events may be full of complex patterns and proper big data aggregation can aid your overall business logic. Tracking notable events and new events offers greater situation awareness and enables brands to take appropriate action in response. Since actions can be triggered by a combination of different events at different times, CEP is an integral part of ongoing hierarchy prescription and aggregate functions.
The following examples denote some of the key benefits of CEP. Notable events from a CEP engine benefit enterprises and organizations in numerous ways. Firstly, greater situation awareness of a brand’s big data allows for smooth business process management. For financial services, you can correlate new events against historical data to spot fraudulent transactions. This heightened level of fraud detection constructs a more robust sensor for your business. Following a series of event queries offers greater levels of flexibility to different brands.
A CEP engine can also synthesize meaningful event aggregation into easily digestible information. By incorporating raw data and domain knowledge, a CEP engine can organize queries and information into high-level concepts. You can track a query’s change of state, become a subscriber to the event cloud, and utilize IoT device information more effectively. While this seems abstract in theory, it’s much more robust in practice. Since so few businesses are readily prepared to leverage data in an impactful way, CEP can streamline data parsing.
Out of the other numerous use cases, hardware design and predictive analytics are especially useful. Predictive analytics can leverage key information in a more meaningful way. Most forms of predictions depend on pattern analysis to develop meaningful insights. However, spotting complex patterns in massive swaths of data is complicated, to say the least. An open-source CEP program can enhance business agility and make it easier for brands to incorporate advanced technology into their business landscapes.
A Multi-Faceted Tool
CEP, in many ways, is still in its infancy. Incorporating a series of event processing protocols into your business is a natural starting point that can enhance your relationship with big data. Between tracking event sources and verifying data aggregation, there are numerous practical applications of CEP technology. Across a variety of industries, CEP is sure to continue its impactful disruption of the data parsing process.