Most importantly, ALE isolates against hardware and vendor changes, smoothes scalability problems, and resolves the complex programmatic synchronization that would otherwise be necessary to share multiple reader resources among back-end applications.
Processing the event stream
ALE isn't the only tool needed to round out a complete RFID infrastructure, however. Making higher-level sense out of the flood of low-level RFID data is not an easy task, and not something that you can entrust to the post-mortem latency inherent in traditional BAM (business activity monitoring) software.
A solution, though, can be found in ESP (event stream processing) and CEP (complex event processing) software. Although CEP-based solutions have been around for a while, primarily in government or military applications, within the past few years viable commercial deployments have begun to appear.
When viewed as a single category, complex event stream processing mines low-level data to infer high-level patterns and trends developing in real time across multiple systems and sources. By collecting event data and infusing it with additional scope -- such as details on location, state, causality, and time reference -- these applications can rake supercharged events across complex rule sets, isolating exceptions and uncovering seemingly unrelated cause-and-effect relationships in real time.
It's one thing to isolate an error, but it?s entirely more relevant to see the whole causal relationship leading up to that error. This is easily achievable using CEP/ESP, and the software can then direct alerts and triggers back into your enterprise systems -- ERP, manufacturing execution, or warehouse management systems, for example.
Sophisticated ESP products, such as Progress for RFID from Progress Software and StreamBase Systems? Stream Processing Engine platform, can tap RFID and additional enterprise systems to build a more thorough event profile. For example, these solutions can easily correlate historic data, such as service-level issues or intrinsic customer value, with real-time RFID-based insight -- say, a purchase order that's been picked but is stuck on the shop floor. Alerts from the ESP system can notify managers that an important order is about to be screwed up again and then display stock that's available on another loading dock, allowing it to be diverted to the higher priority.
Traditional methods of analyzing data -- with polled and scheduled reporting -- impose latencies too great to withstand the real-time surge of data in RFID. Using ESP's in-memory pattern matching and native temporal services, the enterprise is alerted to overarching patterns as they occur. Whether it's perishable fruit rotting in a stalled loading dock or detecting shopping patterns on a retail floor, ESP offers the chance to adjust business rules with real-time agility.
Perhaps ESP's most alluring quality is that its deployments do not require alterations to existing systems. ESP typically runs alongside transaction processing systems, communicating into the enterprise by way of a messaging service or custom adaptor.
Specific to RFID, ESP can improve data validity by scrubbing out the inevitable glitches, collisions, and partial reads. And it's capable of addressing many early-game RFID anomalies, such as flow direction by consolidating added data from motion controllers into discrimination analysis.
As RFID implementation costs drop and tag data becomes increasingly smarter, application sophistication will rise. Accounting for future RFID imperatives -- for example, environmental data or tag updates along a supply-chain route -- will necessitate efficient correlation and analysis. So, although complex event stream processing may not be a requirement for RFID today, it is a smart approach for maintaining insight into tomorrow's highly distributed, real-time networks.