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Discover correlations on the fly with Progress events dashboard 23/02/2007
 
manufacturing business systems Progress Apama, part of application infrastructure and database developer Progress Software, says its high end event correlation software has a significant role to play in bigger ticket manufacturing.



Its systems, which sample any form of data and can show event patterns and correlations at any level, have seen adoption in the financial services sector, but are now ready for use in production.

Mark Palmer, general manager of Progress Apama, says the tools can link directly into data collection and automation systems signalling from the production floor, as well as into packaging, warehousing and all sorts of business sytems, to guide problem resolution and improvement.

“Essentially our systems flip conventional data capture on its head. We put the questions into data streams so that users can see what’s happening in real time, rather than offline after the event,” says Palmer.

So far its systems have been used, for example, to detect bottle filling trending low or high in a large high speed bottling plant. By defining threshold and time window limits, the system provides alarms and dashboard visibility on the fly – as well as comparisons over any period in history.

Data input can be sensor, control, transactional – whatever – and connection taps can be made into PLC and control system data streams, or wherever makes sense.

Palmer says typical users include production managers, business analysts and IT staff. “These systems are providing operational intelligence at a different level,” he insists.

As for costs and effort, he says a typical site licence is around £100,000, with the software running on a standard two-or four-core processor server – while set-up involves minimal training for all user types.

Algorithm and rules choices for seeing patterns in data, he says, are not difficult to grasp, but he does make the point that this is a “white box system, not a black box.” Meaning it’s up to users to drive the system to look anomalies and useful information – it isn’t going to self learn or discover correlations undirected.
 
Author
Brian Tinham
 
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