RoboCrush.com

It’s been an alarmingly fast past June and we blew right past Fathers’ day without a nod.

New models of aggregate trend analysis are unfolding from our initial beta testing and we’ve garnered a bit of underground media interest.

We are attempting to analyze the effects of various variable constraints when applied to model overlays across market sectors. So far the results are ambiguous and disambiguation doesn’t seem possible just through additional testing. It looks like we’ll need a radical refinement of how we approach the modeling aspects in this area.

What we initially thought were statistical outliers look as if they may not be. So we are reexamining our techniques for determining statistical outliers since obviously until we get that right it doesn’t make any sense to start any type of root cause analysis.

Just a quick greeting to all the great mom’s out there to wish you a happy mother’s day - including mine of course - happy mother’s day!

We’ll have some interesting updates a bit later this month, so definitely stay tuned.

Sorry for the lack of updates recently. It’s not for lack of productivity.

As you know we’re beta testing traffic channel specific implementations of RoboCrush and hope to be able to work on merging some of the aggregate trending data in order to create super-aggregrate trending model overlays, although the complexity of that project has grown terribly out of scope.

We’ve been looking quite a bit at the RC-Factor as well now that we are gathering more data on S-Trending and believe we have a better understanding of what the real value of the RC-Factor will be for businesses.

Admittedly we are still in some very theoretical stages at this point so massaging of the various data mining algortithms and their contingent real-world model implementation in order to create business value propositions is to be expected.

What can I say.

Hang tough!

Tad

OK. We’re going to “geek out” on you a bit here. There’s been a lot of buzz recently about model-independent overlays when it comes to collecting and organizing data for feeds into aggregate trending models. As in any good system the separation of data from the mechanism for manipulating the data is key although in certain instances there is cohesion that can’t be completely avoided.

However, there can be sets of overlays that can be tied to specific instances of data organization and theoretically the data modeling can be optimized in such a way to maximize the number of potential overlays within certain overlay constraints. However, once again, the data and the overlays can’t be totally decoupled. That’s not hard to grasp from a “common sense” perspective, but as it turns out it’s quite difficult to prove conclusively.

Once again the time-factor is non-trivial in relation to the coupling/decoupling of data/overlay connections (also known as “n-realms”). Of course when dealing with any type of data collection that is derived from or has a bearing on specific markets and sub-markets naturally the element of time will be an important factor. But what’s surprising is the WAY in which time is a factor when manipulating data/overlay connections.