Thanks to Patrick Austerman from the TIBCO Netrics team for an interesting update on the Matching Engine and Machine Learning Engine. Currently this bipartite graph-based technology is used to identify matches to inputs, typically as a kind of search, in processes like book retailers (“identify a book”) and government (“identify a citizen”).
Clearly this could have huge ramifications for any BPM or CRM use case that involves identifying records and cases via form-filling – text matching to improve accuracy and speed in customer record look-ups. Imagine the effect on call center productivity if agents can quickly and accurately identify callers and cases? Indeed, Netrics’ use in TIBCO CIM (MDM) can be considered an example of this. Not surprisingly, Netrics’ heritage is interactive marketing where identifying and associating web users is of paramount importance.
Other relevant domains include analytics (for example alongside TIBCO Spotfire) and of course CEP (for text pattern detection). In the latter case the matching engine might be useful where there is any possiblity of inaccurate information in an event – consider a manually entered field, or barcode scanning error. When combined with rules to eliminate false positive matches, this could be very useful.
What the Matching Engine does is to learn text (and optionally numeric and date data) associations, regardless of locale, language, etc. But the Machine Learning Engine is another type of decision engine, used for where information is missing and cues must be taken from the absence of that information (in other words, many fields but sparse data). So far it is used in scoring applications.
Overall then we might expect Netrics “matching technologies” to become de rigeur in best-practice BPM solutions, with a strong possibility of some interesting applications in the CEP space… we’ll see if this pans out in 2011!
